{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- This should be added to the overrides/main.html and improved-->\n",
    "<div class=\"grid cards\" markdown>\n",
    "\n",
    "- <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"50\" width=\"50\" viewBox=\"0 0 488 512\"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path fill=\"#2094F3\" d=\"M488 261.8C488 403.3 391.1 504 248 504 110.8 504 0 393.2 0 256S110.8 8 248 8c66.8 0 123 24.5 166.3 64.9l-67.5 64.9C258.5 52.6 94.3 116.6 94.3 256c0 86.5 69.1 156.6 153.7 156.6 98.2 0 135-70.4 140.8-106.9H248v-85.3h236.1c2.3 12.7 3.9 24.9 3.9 41.4z\"/></svg>\n",
    "<a href=\"https://colab.research.google.com/github/AmbiqAI/heartkit/blob/main/docs/guides/train-ecg-segmentation.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
    "    View in Colab\n",
    "</a>\n",
    "\n",
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    "<a href=\"https://github.com/AmbiqAI/heartkit/blob/main/docs/guides/train-ecg-segmentation.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
    "    GitHub source\n",
    "</a>\n",
    "\n",
    "</div>"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train ECG Segmentation Model\n",
    "\n",
    "__Date created:__ 2024/07/17 \n",
    "\n",
    "__Last Modified:__ 2024/07/17 \n",
    "\n",
    "__Description:__ Train, evaluate, and export 4-stage ECG segmentation model from scratch\n",
    "\n",
    "## Overview\n",
    "\n",
    "In this guide, we will train a model to segment an ECG signal into four classes: `NONE`, `PWAVE`, `QRS`, and `TWAVE`. We will use both synthetic and real ECG datasets to train a TCN style model. We will also showcase evaluating and exporting the model for inference via TF Lite and TFLM.\n",
    "\n",
    "__Input__\n",
    "\n",
    "- **Sensor**: ECG\n",
    "- **Location**: Wrist\n",
    "- **Sampling Rate**: 100 Hz\n",
    "- **Frame Size**: 2.56 seconds\n",
    "\n",
    "__Class Mapping__\n",
    "\n",
    "Segment ECG signal into one of the following classes:\n",
    "\n",
    "| Base Class       | Target Class | Label        |\n",
    "| ---------------- | ------------ | ------------ |\n",
    "| 0-NONE           | 0            | NONE         |\n",
    "| 1-PWAVE          | 1            | PWAVE        |\n",
    "| 2-QRS            | 2            | QRS          |\n",
    "| 3-TWAVE          | 3            | TWAVE        |\n",
    "\n",
    "\n",
    "__Datasets__\n",
    "\n",
    "- **[Synthetic](https://ambiqai.github.io/heartkit/datasets/synthetic/)**: Synthetic ECG signals from PhysioKit\n",
    "- **[LUDB](https://ambiqai.github.io/heartkit/datasets/ludb/)**: Lobachevsky University Electrocardiography database consists of 200 10-second 12-lead records. The boundaries and peaks of P, T waves and QRS complexes were manually annotated by cardiologists. Each record is annotated with the corresponding diagnosis.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install -q --disable-pip-version-check heartkit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "import IPython\n",
    "import contextlib\n",
    "from pathlib import Path\n",
    "import tempfile\n",
    "import keras\n",
    "import heartkit as hk\n",
    "import physiokit as pk\n",
    "import numpy as np\n",
    "import neuralspot_edge as nse\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.io as pio\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Be sure to set the dataset path to the correct location\n",
    "datasets_dir = Path(os.getenv('HK_DATASET_PATH', './datasets'))\n",
    "\n",
    "plot_theme = hk.utils.dark_theme\n",
    "nse.utils.silence_tensorflow()\n",
    "hk.utils.setup_plotting(plot_theme)\n",
    "logger = nse.utils.setup_logger(__name__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target datasets\n",
    "\n",
    "The only real-world public dataset containing ECG signals with annotated segments is the [LUDB dataset](https://ambiqai.github.io/heartkit/datasets/ludb/). We will use this dataset to train our model. In addition, we will leverage the synthetic dataset provided by PhysioKit to increase amount of data to train on. We will apply several augmentation techniques to the synthetic dataset to increase the diversity of the data. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = [\n",
    "    hk.NamedParams(\n",
    "        name=\"ecg-synthetic\",\n",
    "        params=dict(\n",
    "            num_pts=5000,\n",
    "            params=dict(\n",
    "                presets=[\"SR\", \"AFIB\", \"ant_STEMI\", \"LAHB\", \"LPHB\", \"high_take_off\", \"LBBB\", \"random_morphology\"],\n",
    "                preset_weights=[8, 4, 1, 1, 1, 1, 1, 0],\n",
    "                duration=10,\n",
    "                sample_rate=100,\n",
    "                heart_rate=[40, 160],\n",
    "                impedance=[1, 2],\n",
    "                p_multiplier=[0.7, 1.3],\n",
    "                t_multiplier=[0.7, 1.3],\n",
    "                noise_multiplier=[0, 0.01],\n",
    "                voltage_factor=[800, 1000]\n",
    "            )\n",
    "        )\n",
    "    ),\n",
    "    hk.NamedParams(\n",
    "        name=\"ludb\",\n",
    "        params=dict(\n",
    "            path=datasets_dir / \"ludb\",\n",
    "        )\n",
    "    )\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target classes\n",
    "\n",
    "For this task, we are going to delineate ECG signals into one of four classes: \n",
    "\n",
    "* __None__: Background signal\n",
    "* __P-Wave__: Atrial depolarization\n",
    "* __QRS__: Ventricular depolarization\n",
    "* __T-Wave__: Ventricular repolarization\n",
    "\n",
    "HeartKit already provides a number of heart segments. We will provide a class mapping for the four classes we are interested in. We will also provide class names for display purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_map = {\n",
    "    hk.tasks.HKSegment.normal: 0,\n",
    "    hk.tasks.HKSegment.pwave: 1,\n",
    "    hk.tasks.HKSegment.qrs: 2,\n",
    "    hk.tasks.HKSegment.twave: 3,\n",
    "    hk.tasks.HKSegment.uwave: 0,\n",
    "    hk.tasks.HKSegment.noise: 0\n",
    "}\n",
    "\n",
    "class_names=[\n",
    "    \"NONE\",\n",
    "    \"P-WAVE\",\n",
    "    \"QRS\",\n",
    "    \"T-WAVE\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define TCN model architecture\n",
    "\n",
    "For this task, we are going to leverage a customized __TCN__ model architecture that is smaller and can handle 1D signals. The model consists of 4 TCN blocks with a depth of 1. Each block leverages dilated depthwise-separable convolutions along with inverted expansion and squeeze and excitation layers. The model is followed by a 1D convolutional layer and a final dense layer for regression. Unlike vision tasks, we leverage larger kernel sizes and strides to capture temporal dependencies in the ECG signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "architecture = hk.NamedParams(\n",
    "    name=\"tcn\",\n",
    "    params=dict(\n",
    "        input_kernel=(1, 7),\n",
    "        input_norm=\"batch\",\n",
    "        blocks=[\n",
    "            dict(depth=1, branch=1, filters=16, kernel=(1, 7), dilation=(1, 1), dropout=0.1, ex_ratio=1, se_ratio=0, norm=\"batch\"),\n",
    "            dict(depth=1, branch=1, filters=24, kernel=(1, 7), dilation=(1, 2), dropout=0.1, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n",
    "            dict(depth=1, branch=1, filters=32, kernel=(1, 7), dilation=(1, 4), dropout=0.1, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n",
    "            dict(depth=1, branch=1, filters=48, kernel=(1, 7), dilation=(1, 8), dropout=0.1, ex_ratio=1, se_ratio=2, norm=\"batch\")\n",
    "        ],\n",
    "        output_kernel=(1, 7),\n",
    "        include_top=True,\n",
    "        use_logits=True,\n",
    "        model_name=\"tcn\"\n",
    "    )\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess pipeline\n",
    "\n",
    "We will preprocess the ECG signals by applying the following steps:\n",
    "* Apply bandpass filter with cutoff frequencies of 1Hz and 30Hz\n",
    "* Apply Z-score normalization w/ epsilon to avoid division by zero\n",
    "\n",
    "The task accepts a list of preprocessing functions that will be applied to the input data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocesses = [hk.NamedParams(\n",
    "    name=\"layer_norm\",\n",
    "    params=dict(\n",
    "        epsilon=0.01,\n",
    "        name=\"znorm\"\n",
    "    )\n",
    ")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Augmentation pipeline\n",
    "\n",
    "We will apply the following augmentations to the ECG signals:\n",
    "* Baseline wander: Simulate baseline wander by adding a random frequency sinusoidal signal to the ECG signal\n",
    "* Powerline noise: Simulate powerline noise by adding a 50 Hz sinusoidal signal to the ECG signal\n",
    "* Burst noise: Simulate burst noise by randomly injecting burst of high frequency noise to the ECG signal\n",
    "* Noise sources: Apply several noises at given frequencies to the ECG signal\n",
    "* Lead noise: Simulate lead noise by adding a random frequency sinusoidal signal to the ECG signal\n",
    "* NSTDB: Add real noise captured from NSTDB dataset to the ECG signal. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "augmentations = [hk.NamedParams(\n",
    "    name=\"random_noise_distortion\",\n",
    "    params=dict(\n",
    "        amplitude=[0, 0.5],\n",
    "        frequency=[0.5, 1.5],\n",
    "        name=\"baseline_wander\"\n",
    "    )\n",
    "), hk.NamedParams(\n",
    "    name=\"random_sine_wave\",\n",
    "    params=dict(\n",
    "        amplitude=[0, 0.05],\n",
    "        frequency=[45, 50],\n",
    "        auto_vectorize=False,\n",
    "        name=\"powerline_noise\"\n",
    "    )\n",
    "), hk.NamedParams(\n",
    "    name=\"amplitude_warp\",\n",
    "    params=dict(\n",
    "        amplitude=[0.9, 1.1],\n",
    "        frequency=[0.5, 1.5],\n",
    "        name=\"amplitude_warp\"\n",
    "    )\n",
    "), hk.NamedParams(\n",
    "    name=\"random_noise\",\n",
    "    params=dict(\n",
    "        factor=[0, 0.025],\n",
    "        name=\"random_noise\"\n",
    "    )\n",
    "), hk.NamedParams(\n",
    "    name=\"random_background_noise\",\n",
    "    params=dict(\n",
    "        amplitude=[0, 0.025],\n",
    "        num_noises=1,\n",
    "        name=\"nstdb\"\n",
    "    )\n",
    ")]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task configuration\n",
    "\n",
    "Here we provide the complete configuration for the task. This includes the dataset configuration, preprocessing pipeline, model architecture, and training parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = hk.HKTaskParams(\n",
    "    # Common arguments\n",
    "    name=\"hk-ecg-segmentation\",\n",
    "    job_dir=Path(tempfile.gettempdir()) / \"hk-ecg-segmentation\",\n",
    "    # Dataset arguments\n",
    "    datasets=datasets,\n",
    "    # Signal arguments\n",
    "    sampling_rate=100,\n",
    "    frame_size=256,\n",
    "    # Dataloader arguments\n",
    "    samples_per_patient=25,\n",
    "    val_samples_per_patient=10,\n",
    "    test_samples_per_patient=10,\n",
    "    # Preprocessing/Augmentation arguments\n",
    "    preprocesses=preprocesses,\n",
    "    augmentations=augmentations,\n",
    "    # Class arguments\n",
    "    num_classes=len(class_names),\n",
    "    class_map=class_map,\n",
    "    class_names=class_names,\n",
    "    # Split arguments\n",
    "    val_patients=0.1,\n",
    "    val_size=20000,\n",
    "    test_size=20000,\n",
    "    val_file=\"val.pkl\",\n",
    "    test_file=\"val.pkl\",\n",
    "    # Model arguments\n",
    "    model_file=\"model.keras\",\n",
    "    architecture=architecture,\n",
    "    # Training parameters\n",
    "    lr_rate=1e-3,\n",
    "    lr_cycles=1,\n",
    "    batch_size=256,\n",
    "    buffer_size=50000,\n",
    "    epochs=100,\n",
    "    steps_per_epoch=50,\n",
    "    val_metric=\"loss\",\n",
    "    class_weights=\"balanced\",\n",
    "    # Evaluation arguments\n",
    "    threshold=0.5,\n",
    "    val_metric_threshold=0.98,\n",
    "    # Export parameters\n",
    "    tflm_var_name=\"ecg_rhythm_flatbuffer\",\n",
    "    tflm_file=\"ecg_rhythm_flatbuffer.h\",\n",
    "    # Demo params\n",
    "    backend=\"pc\",\n",
    "    demo_size=800,\n",
    "    display_report=False,\n",
    "    # Extra arguments\n",
    "    verbose=1,\n",
    "    seed=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load segmentation task \n",
    "\n",
    "HeartKit provides a __TaskFactory__ that includes a number ready-to-use tasks. Each task provides methods for training, evaluating, exporting, and demoing. We will grab the __segmentation__ task and configure it for our use case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "task = hk.TaskFactory.get(\"segmentation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the datasets\n",
    "\n",
    "We will download the synthetic and LUDB datasets using the `heartkit` library. If already downloaded, this step will be skipped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "task.download(params=params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the data\n",
    "\n",
    "Let's visualize a sample ECG signal from the synthetic dataset. Note this contains no noise or artifacts. Augmentations will be applied later to generate noisy samples for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ecg, segs, fids = pk.ecg.synthesize(\n",
    "    signal_length=params.frame_size,\n",
    "    sample_rate=params.sampling_rate,\n",
    "    heart_rate=60,\n",
    "    leads=1,\n",
    "    preset=pk.ecg.EcgPreset.SR,\n",
    "    noise_multiplier=0.0\n",
    ")\n",
    "ecg = ecg.squeeze()\n",
    "segs = segs.squeeze()\n",
    "pwaves = np.where(segs == hk.tasks.HKSegment.pwave, ecg, np.nan)\n",
    "qrs = np.where(segs == hk.tasks.HKSegment.qrs, ecg, np.nan)\n",
    "twaves = np.where(segs == hk.tasks.HKSegment.twave, ecg, np.nan)\n",
    "\n",
    "\n",
    "ts = np.arange(0, len(ecg)) / params.sampling_rate\n",
    "fig, ax = plt.subplots(1, 1, figsize=(9, 4))\n",
    "plt.plot(ts, ecg, color=plot_theme.primary_color, lw=2, label=\"ECG\")\n",
    "plt.plot(ts, pwaves, color=plot_theme.secondary_color, lw=3, label=\"P-Wave\")\n",
    "plt.plot(ts, qrs, color=plot_theme.tertiary_color, lw=3, label=\"QRS\")\n",
    "plt.plot(ts, twaves, color=plot_theme.quaternary_color, lw=3, label=\"T-Wave\")\n",
    "plt.legend()\n",
    "\n",
    "# Plot segments\n",
    "plt.title(\"Synthetic ECG w/ Segments\")\n",
    "ax.set_xlabel(\"Time (s)\")\n",
    "ax.set_ylabel(\"Amplitude\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the augmentations\n",
    "\n",
    "Taking the existing synthetic ECG signal, let's look at the effects of the augmentations on the signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723838702.560559  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723838702.580638  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723838702.580743  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723838702.581841  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723838702.581918  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723838702.581964  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723838702.624118  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723838702.624214  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723838702.624283  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
     ]
    }
   ],
   "source": [
    "augmenter = hk.datasets.create_augmentation_pipeline(\n",
    "    augmentations=params.augmentations,\n",
    "    sampling_rate=params.sampling_rate,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ecg_noise = augmenter(ecg.reshape(-1, 1)).numpy().squeeze()\n",
    "\n",
    "pwaves = np.where(segs == hk.tasks.HKSegment.pwave, ecg_noise, np.nan)\n",
    "qrs = np.where(segs == hk.tasks.HKSegment.qrs, ecg_noise, np.nan)\n",
    "twaves = np.where(segs == hk.tasks.HKSegment.twave, ecg_noise, np.nan)\n",
    "\n",
    "ts = np.arange(0, len(ecg_noise)) / params.sampling_rate\n",
    "fig, ax = plt.subplots(1, 1, figsize=(9, 4))\n",
    "plt.plot(ts, ecg_noise, color=plot_theme.primary_color, lw=2, label=\"ECG\")\n",
    "plt.plot(ts, pwaves, color=plot_theme.secondary_color, lw=3, label=\"P-Wave\")\n",
    "plt.plot(ts, qrs, color=plot_theme.tertiary_color, lw=3, label=\"QRS\")\n",
    "plt.plot(ts, twaves, color=plot_theme.quaternary_color, lw=3, label=\"T-Wave\")\n",
    "plt.legend()\n",
    "\n",
    "# Plot segments\n",
    "plt.title(\"Synthetic ECG w/ Noise\")\n",
    "ax.set_xlabel(\"Time (s)\")\n",
    "ax.set_ylabel(\"Amplitude\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the model\n",
    "\n",
    "Lets quickly instantiate and visualize the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"TCN\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"TCN\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ inputs (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ reshape (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ inputs[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ ENC.CN              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span> │ reshape[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ ENC.BN              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span> │ ENC.CN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.DW.B1.CN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span> │ ENC.BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.DW.B1.BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span> │ B1.D1.DW.B1.CN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.DW.ACT        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ B1.D1.DW.B1.BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.PW.B1.CN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> │ B1.D1.DW.ACT[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.PW.B1.BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ B1.D1.PW.B1.CN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.PW.ACT        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ B1.D1.PW.B1.BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.DROP             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ B1.D1.PW.ACT[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SpatialDropout2D</span>)  │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ inputs (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ reshape (\u001b[38;5;33mReshape\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ inputs[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ ENC.CN              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │          \u001b[38;5;34m7\u001b[0m │ reshape[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ ENC.BN              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │          \u001b[38;5;34m4\u001b[0m │ ENC.CN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.DW.B1.CN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │          \u001b[38;5;34m7\u001b[0m │ ENC.BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.DW.B1.BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │          \u001b[38;5;34m4\u001b[0m │ B1.D1.DW.B1.CN[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.DW.ACT        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ B1.D1.DW.B1.BN[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.PW.B1.CN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m,    │         \u001b[38;5;34m16\u001b[0m │ B1.D1.DW.ACT[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.PW.B1.BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m,    │         \u001b[38;5;34m64\u001b[0m │ B1.D1.PW.B1.CN[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.D1.PW.ACT        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ B1.D1.PW.B1.BN[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ B1.DROP             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ B1.D1.PW.ACT[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mSpatialDropout2D\u001b[0m)  │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,310</span> (28.55 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,310\u001b[0m (28.55 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,922</span> (27.04 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,922\u001b[0m (27.04 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">388</span> (1.52 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m388\u001b[0m (1.52 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = nse.models.tcn.tcn_from_object(\n",
    "    x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"),\n",
    "    params=architecture.params,\n",
    "    num_classes=len(class_names)\n",
    ")\n",
    "model.summary(layer_range=('inputs', model.layers[10].name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model\n",
    "\n",
    "Using the task configuration, we will train the model on the synthetic and LUDB datasets. We will also apply augmentations to the synthetic dataset to increase the diversity of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Creating synthetic dataset cache with <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5000</span> patients                                   <a href=\"file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">ecg_synthetic.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">159</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Creating synthetic dataset cache with \u001b[1;36m5000\u001b[0m patients                                   \u001b]8;id=172088;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\u001b\\\u001b[2mecg_synthetic.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=461477;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\u001b\\\u001b[2m159\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building ecg-synthetic cache: 100%|██████████| 5000/5000 [00:56<00:00, 87.92it/s] \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Validation steps per epoch: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">78</span>                                                             <a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/datasets.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">datasets.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/datasets.py#107\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">107</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Validation steps per epoch: \u001b[1;36m78\u001b[0m                                                             \u001b]8;id=284434;file:///workspaces/heartkit/heartkit/tasks/segmentation/datasets.py\u001b\\\u001b[2mdatasets.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=866120;file:///workspaces/heartkit/heartkit/tasks/segmentation/datasets.py#107\u001b\\\u001b[2m107\u001b[0m\u001b]8;;\u001b\\\n"
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     "metadata": {},
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:   0%|           0/100 ETA: ?s,  ?epochs/sWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723838774.747244  758430 service.cc:146] XLA service 0x7b7988001e40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1723838774.747268  758430 service.cc:154]   StreamExecutor device (0): NVIDIA GeForce RTX 4090, Compute Capability 8.9\n",
      "I0000 00:00:1723838783.452909  758430 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
      "Training: 100%|██████████ 100/100 ETA: 00:00s,   1.96s/epochs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 743us/step\n",
      "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - acc: 0.8512 - f1: 0.8524 - loss: 0.1304\n"
     ]
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>VAL SET<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">ACC</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8510</span>, <span style=\"color: #808000; text-decoration-color: #808000\">F1</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8522</span>, <span style=\"color: #808000; text-decoration-color: #808000\">LOSS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1312</span>                                                  <a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/train.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">train.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/train.py#184\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">184</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mVAL SET\u001b[1m]\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8510\u001b[0m, \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8522\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1312\u001b[0m                                                  \u001b]8;id=904348;file:///workspaces/heartkit/heartkit/tasks/segmentation/train.py\u001b\\\u001b[2mtrain.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=191133;file:///workspaces/heartkit/heartkit/tasks/segmentation/train.py#184\u001b\\\u001b[2m184\u001b[0m\u001b]8;;\u001b\\\n"
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",
      "text/plain": [
       "<Figure size 900x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task.train(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model evaluation\n",
    "\n",
    "Now that we have trained the model, we will evaluate the model on the test dataset. Similar to training, we will provide the high-level configuration to the task process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Creating synthetic dataset cache with <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5000</span> patients                                   <a href=\"file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">ecg_synthetic.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">159</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Creating synthetic dataset cache with \u001b[1;36m5000\u001b[0m patients                                   \u001b]8;id=288389;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\u001b\\\u001b[2mecg_synthetic.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=256787;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\u001b\\\u001b[2m159\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building ecg-synthetic cache: 100%|██████████| 5000/5000 [00:57<00:00, 87.27it/s] \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 970us/step - acc: 0.8674 - f1: 0.8687 - loss: 0.1110\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>TEST SET<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">ACC</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8652</span>, <span style=\"color: #808000; text-decoration-color: #808000\">F1</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8665</span>, <span style=\"color: #808000; text-decoration-color: #808000\">LOSS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1141</span>                                               <a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/evaluate.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">evaluate.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/evaluate.py#47\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">47</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mTEST SET\u001b[1m]\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8652\u001b[0m, \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8665\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1141\u001b[0m                                               \u001b]8;id=630320;file:///workspaces/heartkit/heartkit/tasks/segmentation/evaluate.py\u001b\\\u001b[2mevaluate.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=347514;file:///workspaces/heartkit/heartkit/tasks/segmentation/evaluate.py#47\u001b\\\u001b[2m47\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
     ]
    }
   ],
   "source": [
    "task.evaluate(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix\n",
    "\n",
    "Let's visualize the confusion matrix to understand the model's performance on each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5Nq3GLuKl/2vaVylCl5rF8fENoN34vzl/6+FXvSeAK/cekSFZAiwtjf8DnyVFIvxfB3Ln0VMAzly7R0BgEEnju9L791UU6DyW/J3H0vzXBVy5++ir+/FfZ2trCxDuR/vb8fZZs2d6r65NhE8B/f3CgoasOcLqOrs4kSNXVs6eusiP/btx8fZh/rl/jAOnNlOlRvnP6lf8+HF5+uSZUdmRAydInzEtP/zcheQpk5I8RRK69W5PthyZmTFZq+GZmwP7w7JU06b/StZsGUmc2I1atSvTuk1jZvw+H19fPzJkTEuiRAm5eOEKk6aMwMPrAo8eX+LQ0U0ULWb83WVnZxthcOnr64ednR2ZMqUzKq9RsyL58+diYP9fP9jHEiUL8+KFD4kSJeTk6R14eF3kgcc5Jkwchp2d7Tf4FCQqOTg6cP3eca7fO8HlW4cZNXZAuB/1xUqEZcwDXr9m6+7l3HI/zS3308yYM55YsWJ+8hrx44cNvX7yr+8rCJurFDt2LBIlTkjFKmXo2KUV9+4+4NbNu0bXv37tJqXLFuPUxd3cfHCSy7cO8+Mv3RQAi9kwu+FVLVu2jOoufHObjl7E87kPnaoX/az6P85czaGLYeOKbaytqFMsJ+2qFP6qPvy98wSTV+0xvM6fMQVDWhhPgmtXpTAPHr9g9qZD/LHxEAD2tjaM61iLkjmM/0OcPXViyuXJSOK4sfB67sPSPaf4efY6XvoFUK9ELiAs2Bq+cAt50ydjWvf6hi++OsVyUnvQbKat2cuMng2/6n09fvGS3GmThiuPGyvsiZTX85ekTRKfO55hX+RTVu8hSbxYDGtVlZd+Acxcf4B24/9m5ZC2H50bIx/3NmuRJ39Oo8ng+QqG3QsJ3eIb1c1XMDdOzo68eulrKM9TwLhu8hRJsbS0pFqtCgQFBTNy8AR8vF/SqkNjps4eg4/PS/buPPjBPuUrkItcebMzefwso/JJ42aSNHliuvZqS/feYfMBfF/50r55L7Zv3v0Vn4JEhh3b9zFsyHh+6NOJylXKGsrHjp7KsKETgLCJ5gCdu7Ti2bMXdO/WH4DevTuxas1cShSrycULVwC4du0WefPmxNLSkpA3TwttbGzIkzcHAG6J3o1/t7e3Y/jIn5k29U/u3n1AsuTGC3q8lTpNCqytrVi8dCZ/zV/G4EFjKVo0Px06tSBWrBi0atH9m34m8v088vBi2qQ5nD97CQtLS0qVKULLto3IlCU9tao0Jzg4bLRAytTJAZg19zd279jP5N9mkTlLBrr2bEuixAmpVqHxxy5D5+6t8X7hw64d+8Mdq1S1LDP/HG94febUeXp0+cVwbYCUqZITHBLMxGkjmT55DhcvXKFSlbL06tMRaysrRg797Vt8HCJfxeyCjgULFkR1F76pW+5PGPX3NrKlTkzVQlk/fQLQrVZJmpbLz6On3qw/fJ7A4GCCg0PgK+Y5V8iXiUzJE/LMx5d9567z1MeXgPeGlwDYWFuTPIErZXJloHSu9ASHhLBq/xl+mb2eGT0bkC11YkPd+X2bGZ1bo0h2Gg6fy5TVe6lWKCv2tjb8c+8Rdz2f0bZyYZ6/NB76ki9jcjYevkBISCiWlhYEBgXz0s/46XdQcDCBQUE88/E1Ko/p5GDIbAS8DsLGJvwwMzvrsFvb/80QKr83K4JYALN6NcLRPuzpY4akCWj26wKW7j5Jl5rFP+uzlPAunLvMqRNn6ditFR7unhzef4w06VMxYlx/Xr8OxN7BzlD3r7nLKFuxJNPmjGPs8Mn4+vrRrHV9suXIDIRNngQMw1xix3GlWtlGnDl5HoDtW3Zz8PQWuv3Q7oNBR5y4sZn8x2ju3XnAjMl/Gh17HfCamzfusGnddjZv2IGVpRWNmtdh0oxRNK7djtMnPjwfQKLG3bv3OXTwGGvXbOHp0+eUr1CSH/p04tEjL2bN/Atn57B7xdnFiSKFqvLggTsA+/Yc5sz5XfTo2Y62rcPmYsyetZCJk4cz7fdfmfjbLCwtLfnxx84kTBj2pNnhvXu11w8dsbGxZvzY6R/tn7OTI05Ojsz+YxE/9gnLwK9ftxUbW1tat2nEiGG/cePG7W/9sch38O8f62tXbeLG9dv8PLAnVaqXZ+2qTcC776uzp87Tpf1PAGxctx1fXz/6D/7BaK7Gv3Xr1Y7iJQvxU68heL8IP0H94P6j1K3eipgxXShavCCZsqTH0dE40+Lk7IiVlRXDB41n6qTZhuu7usakTYemTJow0+ghj3wj0XSYU1Qxu6Dj/8njFy/pOmUZzg52jOtQ0zBh+1MyJHu3AlTlAlloMOxPBs7dwLiOtb64L4nixCRRnLAUb8X8mRm6YDPtf1vCmmHtDEOsfv17G+duPmDJgFaGH/Xl8mSk9qA/GLN0Owt/bvHB9m2srWhQMjfDF27h8h0PcqZNyp1HYdmFAXM/vAzpSz9/Yjg5cObGfdqO+zvc8bM3HrDluPEylhtHdSRx3FgA2NlaExgYHO68gDdLCdq/mYtiZxP2HotlT2sIOACypU5M4rixOHvjwQf7KJ+nffNeTJ8zlvFThwEQFBTE7OkLyF84D6nSpDDU27PjAAN+HEnfgT3YvHc5ALdu3GHsiMn8MuQHfF+F/Yfx7RCYu7fvGwIOAN9XfuzYupeadatgZWVl9LQPwoZCzF08FSdnR2pXah5ursewMT+TM082KpWoZ5jwu2HNVnYcWs3gUT9RvezHn0jK91W7ThUmTRlJruylDStQrV+3FUtLS4YM+4kVy9fj9+aBxZEjJw0BB8D9+w85fPgE+fLnMpT9OedvEidxo3uPtjRuUgeAUyfPMem3WfT5qQsv3/wwS5YsMd16tOWHXoN49erjP9b83tyrK5avMypfvmwtrds0Il/+nAo6/o/Mmj7fsCzu26DD/809uHrlJqO6q1dspP/gH8ibP2eEQUf1mhXp2787ixasYP6fSyK83mOvJ4ZzN6zbRrde7Vi2eg4Fc1cwzEvx9/PHydmJ1Ss3Gl9/5SZKlS1G1myZOHLoxNe9cZGvZHZBR82a4dfD/pTVq1dHQk++jo+vP50nLcPH158/f2xK/Fgunz4pAjbWVhTPnpa5Ww7j/zrQaA7G1yiTOz2r9p/h1NV7FMqSisCgYNYcPEvz8gWM5kfYWFtROGtqlu46SWBQ8EcnryeIHfYeX7wK+w/w2x90PeuUIn3S+BGe4/BmvHP6JPGZ0dN4lY4Jy3cRJ4YTzcsbL2UYN6az0T97vXgZrt3Hz18BGIZMvf3fODHCTwSM7eKIt68mEH+tR+6e1K7UnBSpkhE/flxu3byDl+cTjl/cya3rd4zqzp+9mGV/ryFj5nQEvg7k4vkrNGgaFlTffFP3kYcXAF5e4SexP/F6iq2tDY6ODvj4vPv3b2NjzawFv5Ehczqa1unA1cvXjc6zsbGmfpOazJg812iFoaCgIPbsPEDzNg2xsbEmUIsMmI02bZtw7uxFo302ADZt3EGTpnXIlj0z7u5h87IiWvDgsecTsmXLbFQ2bMh4pkz6gwwZ0+Ht7cOli/8wcHBvAK5fDxva+suAnrg/fMSBfUdIliwsy5sgQVg2JG7c2CRLlph79x4SGhqKh7snmTKlDzfZ/e29+zlj+iX68PcP4NnT58Ryfffv1cPDEyDc5PTHb+6BmLFihGunWIlCTJ7xKzu27eXHnoM/+/ob1m7j54E9qVCpFH/NW/bm+l6kTuNk0vXl64WGhn/oKR9mdkHHihUrCA0N/eTEp7c/GEJDQ7GxMa/9FQICg+g+dQV3Hj1lZq8GpE4U96vbCw0FX//X3yzoeLvilM+bpzPPX/oRFBxiGOP8vqDgYEJCQwkOCcGGDwcdD7yeA+DqEjZhPsmbZWmdHGwpkOnjK43FcHIIV8fF0Z64MZ0/em76pAk4fe2eYZjWW+dvPcTe1sawT0emNxsFej4Ln7r2evGSFAljhyuXL3P75l1uv5ngmDZ9KhK4xWd5BBsJ+vn6cer4WcPrIsUL4Ofrx4mjp4GwoMPTw8toPshbCRLGw9/Pn5cvXxnKLCws+O33kRQulp9OrXpzNIKneq6xY2FjY4OVVfj72NraGisrKyytrEBBh9mIHz8uz5+/CFdu8yaLaW1txaWL//D69Wvc3MLvE5TQLQFPHj8NV/78uTdHDr+7R0qWLMz9+w+5+s8NAJIkSUTqNCk4f2lfuHN/mxSWzUuaKDsvXvhw+vQFSpUuSqJECY32+Xjbn8cRXF+iLydnR2LHcTW6r86dCVtIxS2R8T2YIGHY99e/78GcubMxd+Fkzp6+QLsWPcNlbD/m7XDVGDHePcw8d+YiqdOkIKFbAu7eebc65Nvvz4j+PyDyvZnd6lUlS5akVKlSlCxZ8oN/jRo14uzZs1hYWET4IzkqBYeE8NPMNZy7+YCxHWqQPXXEEw+9nr/klvsToyVrn3q/ClfP29efnaf+IWHsGMSO4Cn9pzz1iXhYwJoDZ7GwgIzJw74gY8dwxMXRnl2nrxr1ydf/NfvOXidlwjiGgCeiNl/5B7Box3FcnR3IlNwNgEzJ3UgaLxYLth7F1/91uHM+1DdTlM2dnifer9h5+h9D2TMfX7afvELx7GkMy/+mSBiHdEnis+fsNaM5Iocu3sTjqfcngyIxnYWFBT8P7oXvK18Wzl320bq582WnQpXSLFm42ihzsX7NFhIncaNoiXd7qbjGjkXZSiU5uP+YUbZi6OifqVarIv37jGDLhp0RXuex11NePPemfOVShh+tELbrepkKJbh29SYBH1hfX6LG9eu3yJY9E2nSGP9/tE7dqgQHB3Ph/BVevnzFtq17yF8gF2nfWyI5XfrU5C+Qi927Dnz0GrVqVyZ3nuxMn/YuAzZs6AQa1m9v9DdsSNhk3t8mzKRh/fa8ejN0b/WqsCEtzZrXM2q3eYv6BAYGsn/fEST6sbOzxck5/NKzvfp0wtLSkt07391XWzbtwt8/gPqNaho9NG3cLGwI377dhwxladOlYuGyGdy7+5Cm9Tt+cE+P2LFjRVj+ts0zZy4Yyt7uON6oaW1DmYWFBfUb1eTp0+eGoEgkKpldpmPfvvBPld6KHz8+P/30E+3bt8fW1pb58+czfPjw79i7Txu/bCd7zl6jePY0eL8K21H8fW8305u8ag/rD583mp/QedIyEri6kCVlImLHcMTjqTdrD57D6/lLRrevYdTO1fue7D0TtlHWPa9nvPQL4I8NYZNq0yWNT/HsaQGYvfEgZ67fp3CWVCSMHRPvV37sOPUPF2+707BUbpLFD3vCb2VpSbNy+Zi2Zh9NR82nSsEshISEsubAWR4982FE66qGay/dfZLdp69SPHsaEsaOyeMXL1lz8BweT18wvFVVwxAsS0sLBjavRJdJy6g96A+qFc5G/FgueD734cQ/d3Cyt2Ny17pf9XmXyZ2BbKmOM2juRm4+fEwsZweW7TlFSEgIHasZrxbWp34ZOvy2mJZj/qJ2sZy89Atg4fZjJE8Qm3rFc33gCgLQvE1DYsR0IcGbybZlKhQ3PNGbN+tvfHxeMnjUT9jZ2XHx/BVsbKypXqcSOXJlpVenX3j44N3QmMRJ3Jg+dxzbN+/By/Mx6TKkoUmLuly+eJUxwycZXXfab3OoUr08M+ZPYPb0BXh7v6RJi7rYWFszZti7uq07NKF5mwacOHYGP19/atY13lByy8ad+Pn6ERISwsyp8/ixfzfWbFvEqqXrsbSypEGTWiRKnJBu7fpG1kcoX2jSxFmULVecLduXMmvGAp4+fU6FiqUoV74E8+YuMQxrGTJ4HMVLFGLDpkXM+H0+AB06NufZs+eMe28ieKHCefmpXzd27dzP0yfPyZsvB02a1mH7tj38Pm2eod77WZC3XrzwBsLmgGx8s68CwLmzl1gwfxnNmtfDysqKgweOUaRofmrVrsy4sdMNfRTz0qptI2LEjEHCN9mIshVKGlYvmzNrIbFixWD7vlWsWbGJa9duAlCyVBHKlC/Oru372LLx3cMNL8/HTBo/k59+6cbilX+wZeNOMmVJT5PmdVm1fANnTof9FnBydmTxyj+IFSsGv0/+kzLljRcwuX3rHiePnwGgdv1qNGtZny0bd3Ln9j2cXZwoUaoIJUoVZuvmXRzcd9Rw3paNO9m35zDderUjdhxXLl34hwqVS1OgUB56dx9otC+RfENm9uDb3FkAZr91avz48enbty/t2rXDxsaGhQsXMnz4cG7duvXpkz/Ad2/krMffeuwiTl69+8HjZ/7oB8CAPzeECzqW7D7J1mOXuO3xBB+/AFwc7cmWKhHNyxUgVzrjZWHXHjzHoHkb/908AFULZjXs4n340i0W7zzB5bsePPPxxc7GmrRJ4lOzSHaqFcoabhjbpqMX+Xvnce48ekZgUBBpE8enefn8lHmz2/fbNudvPcL1B148f+mHg50tWVK40bJCgQj3E7ly9xF/bDzIyat38fV/TZyYzmRNmYg6xXJ8dP+Rz9mRHMD7lR+/rdjN7jNX8X8dROYUbvSqW4rMKdzC1T1y6RbT1+7jn3ue2NtaUzRrGnrUKWk0T+RbS19j/KcrmbmDZ7aQNFniCI8Vyl6e+/ceUqdhdVp3aEKKlMkICQ3h7MkLTJkwy2gJXYCYMWMwbuowcubOSkzXmDxy92TDmq1MmTArwtVVkiVPwi9Df6Bw8fzYWFtz6vhZRg2dyLnT757cjZ86nLqNqoc79999fKt67Uq06tCYVKlTYGtrw+VLV5k5ZR6b1+8w9aMxK88DwmdL/x/kzp2Nfr/0IFv2TMSOHYs7t+/z96JVTPxtptGwlOw5MjNk2E/ky5eTkJAQ9u09zIBffjWaxJ0yZTImTBxK9uyZcXZx5s7te/z99yqmTp5DYODHf5gVKZqfTVsW07RxZ9au2Wx0zNramt59OtG4aR3c3OJz7+5D/pj1F9On/X/u/eJoY/fpSmbu+LkdH/xey5utNC9e+DBiTH9y58lOwoTxsLSy4vbNu6xcvp7fp8wlKCj8MMxWbRvRql0TkiVPjOejxyxbspYJo6cb6iZNlojj5yLOxAIs/Xs13Tv9DITdz527tyZXnuzEjReH4KAgrl+/zcpl65kzc2G4IVmOTo707d+d6jUrEss1Jjeu32LqxNmsWv7hxVyig0cvrkR1Fz7Id/ecKLu2Y8nWUXbtL2XWQUeCBAno27cvbdu2xcbGhr/++osRI0Z8VbDxVmQFHSL/9v8QdEj08P8adIj5+X8IOiR6MOugY9cfUXZtx1Jto+zaX8rshldBxMHG8OHDuX37dlR3TURERERETGR2QcfEiRNp27YtVlZWLFiwgBEjRnDnzp1PnygiIiIi8r1oTodJzC7o6Nq1K6GhoVy8eJEECRIwefLkj9YPDQ2lRo0a36dzIiIiIiJiMrMLOu7evUtoaCguLi5kzZr1k/XfXzLzQ2xtbbGz+9f4UwtLCFWEKiIiIiIS2cwu6EiZ8tvvl9CvXz8GDx5sVBZ45wxBd85882uJiIiIyH+AHl6bxOw2B4wMo0aNIkaMGEZ/QXfPRXW3RERERET+E8wu0/E+Nzc3KlSoQMaMGYkRIwY+Pj5cunSJLVu24O7u/tntvH79mtev/7UjdjSPTu88esr0tfs4fe0+3r5+JIwdg4r5MtOsXH4c7MJ2Dp+98RB7z17jvtczXvm/JkHsGBTNmpo2lQsT2yX8Lqv/5uv/mqlr9rLj5D88e+lLkrixaFg6D/VKfHwjvSELNrF6/1mKZk3NlG7vdugNDQ1l5voDrNx/hqDgECrmy0TPOqUMmwm+vWb1ATPpWacUlfJn/sJPRyLbp/bFyJu5NI/cI94QrUKV0lStWYHsObMQL34cHj7wYOe2fUweOxNvb58Ptpk8RRK2H1qDvb0dVUrV59yZS4ZjadOnYtSEgWTKkoGb128zsO8oTh0/a3R+m07NaNCkJuWL1gm3vr2YrwwZ09Lv5+7kyJmFBAni4efrx5Ur15k0cRZbNu/66LkJEsajY6eW5MmTnZy5suLi4kylCg05sP9ouLobN/9N0WIFwpXv2L6XWjVaGl67uSVg8tSRFCyUh4cPPBg4YHS4flStVp6Jk4eRM1vpj97TYl4KFcnLqg0LIjxWqUwDTp04G+Gx91WvWZG2HZuRKXM6AoOCuHrlBr+OmGTYyK9+oxpMmj7qg+d3atvHsK9G3vw5GTGmP6lSJefc2Yv82HMw168ZbxkwfPTPpE6Tkoa1o9/yqf8XNJHcJGYZdNjb2zNu3DjatGmDtbV1uA3sAgMDmT17Nr1798bf3z+Kehl1PJ5602TkfJwd7GhQKjcxnOw5d+MBv6/bz+U7HkzsUgeAy3c9SJ80AeXzZcTJzo5bHo9Ztf8s+8/fYNnAVjjY2X7wGsEhIXScuIRLdzyoXyIXyRLE5tDFm4xctBXvV/60qVwowvMu3nZn/aHz2NmEv7U2Hb3InM2HaVmhAA62NszedIg4MZxoXeldW7M3HSJRnJgKOMzcovnLObD3iFGZhQWMHD+A+/cefjDgAPj1t0E88vBi1fINPLzvToZMaWnepiElyxalUol6BPgHRHjewBE/EhwUPliwtLRk5vyJPH/+gpGDxlOmYklmL5xEsTyVeekTtm9FnLix6d6nPZ1a9VbAEc0kS5YYFxcnFi9ahbv7IxwdHahWvQLLVsymW5efmTd3yQfPTZs2Fb1+6MD1a7e4dPEf8hfI/dFr3b//kCGDxhqVuf/rXp7xxzgSuSVg0IAxFCiYmwULp5EnZxnu3n0AgJ2dLcNH9mPYkAkKOKKpP2Ys4MypC0Zlt29+ehXN3n070+vHTmxYu5Wlf6/Gxsaa9BnT4uaWwFDn8METdG73Y7hz23VqTuYs6dn/5nvVJYYz8/6exqnjZ1k4bxn1G9VkzoLJlCxcnZA3P3TTZ0hD42Z1KV+izte8XZHvxuyCDisrKzZu3EiJEiXYvXs3CxYs4OzZs/j4+ODi4kL27Nlp1qwZHTt2JEOGDJQrV87wf8D/ig2HL+Dj68/cH5uQJnE8AOoUy0lIaCgbDl/A+5UfMZwcGN+xVrhzs6VKTO8Zq9l79joV8mX64DV2nvqHszceMLh5JWoUyQ5AvRK56P37Kv7YeJBaRbMTO4aT0TmhoaGMXrKdKgWycPRK+C/ofeeuUyl/ZjpVLwaAf2AQe89eMwQd9zyf8ffO48zp0+TLPhj5bk4dPxsuk5A3f04cnRxZvXzjR8/t0KIXRw6eMCo7f+YSv/0+kpp1K7Pkr1XhzilWqhDFShVmxpS5dO/d3uhYytTJSZMuJQWyluXhAw9WLF3P2Wv7yJU3O/t2HQLgxwHdOHboJPt3H/6StytRaNvWPWzbuseobOaMBew7uI4uXVt/NOg4c/oCyZPk5NmzF1SvUfGTQYe3tw9Ll6z94HF7ezuKFy9IpQoNOXTwOHNmLyJf/lyULlOMuX8uBqBb97Z4e/swf97Sz3+TYlaOHjrJhnXbTDonV57s9PqxE4P7j2HW9PkfrHf3zn3u3rlvVGZvb8ev4wZyYN9RvDwfA5Anbw4c7O1o07w7AQGv2b1zP8fP7SRlqmTcuH4bgKGj+rFowXKu/nPDtDcoEkXMbk5Hx44dKVGiBJ07d6ZMmTKGoOPmzZucPXuWBQsWUKZMGTp16kTJkiXp2LFjVHf5u3v15klwnH/96I8X0xlLCwuj4Ur/lihuTAB8fD+eITp97R4A5fMaBybl82UiIDCI3WeuhTtnw+EL3HjgRZeaxSNsM+B1EDEc7Q2vYzrZ4/86yPB6/PKdlM+bicwp3D7aNzFP1etUIiQkhLUrNn203r8DDoAtG3cCkCZdqnDHrK2tGTyyL3NnLuTOrXvhjtvbh61M9+KFNwD+fv74+wfg4BB2r2XJlpGadSoztP/YcOdK9BQSEsKD++7EjBXjo/VevnzFs2cvTGrbysoKJ6eIh5/a29thaWnJ8+fehrIXL7xxdHQAwoZe9fyhAz/1GfpZKyuK+XJydsTK6sP/Lf23dh2b4fnoMX/8HjY8y/ED91BEylUoiUsMZ1YtX28os3ewxz/gNQEBYUPD397HDm/utQqVS5MlW0bGjpz62deRSBAaEnV/0ZDZBR1NmzZl7dq1zJgx46P1Zs6cybp162jevPl36pn5yJM+GQBD5m/iyt1HeDz1ZuvxSyzfe5qGpfMYDZsKDQ3lmY8vj1+85NTVe4xevB0rSwvypE/+0Wu8DgrGyjJ8AGNvG5Ycu3zHw6j8lX8Ak1btpnWlQsSN6Rxhm5lTuLHl2CXO3XjAtfuerNh7hswpwwKMw5ducfzKHbp+IGAR82ZtbU2VGuU5eewM9+89NPn8ePHjAvDsybNwx1p3bELMWDGYPH5WhOfevHGHFy+86flTJxIncaN91xY4uzhx4exlAIb82pd5sxdHGLBI9OHo6EDsOK6kTJmMzl1aUbZccfbuOfRNr5EmTUo8vC7g7nmBazeP0n9AT6yt3w0IeP7cm5s3btO7TyeSJ09CvfrVyZYtEyfejPUfNqIv27ft5dDB49+0X/J9TZw2khv3T3Ln0RlWrp9H9hyfHu5btHgBzpw+T5sOTbl44xA3H5zk7JV9tGrb6JPn1qpXBV9fPzau324ou3DuEjFiONOhSwuSJE1En35defHCmxvXbmFra8Pg4T8ybtRUw8MWkejA7IZXZcyYkTlz5nxW3S1btjB27H/v6WXhLKnpXL0YczYfYs/ZdxmHNpUKhcsyPPF+RZneUwyvE7i6MLJNdVK6xfnoNVIkiENwSCjnbz4gZ9qkhvLT18LSwp7Pjccqz1x/EDsbG5qUyfvBNhuVycOhizdp9mvYk6DUieLSoWpRgoJDGLtkB20+ErCIeSteqhCx47gy7gufunXs3oqgoCA2rttuVB4vfhy69W7PiIHjDfMz/s3P149ffhjO2MlDaNe5OUFBQYwa8hsP7rtTvXYlkqdMRvP6nb6oX2I+Roz6hdZtwn7ABQcHs27tVnr3GvTN2r916y779x3h4sV/cHJyoHqNivzYtyup06SkZfNuhnrduv7CXwunUaduVQCmTf2To0dOki9/LqpULUfeXGW/WZ/k+3r9OpANa7eyc/s+njx5Rrr0qenYtRVrNi+kavlGXDh3OcLzYsaMQZy4scmbPxdFihZg/OhpPLjvToPGNRk5dgCBgUH8NW9ZhOfGihWTkqWLsmXjTl699DWU37v7kBGDJ9B/8A8MHv4Tvr5+9OraHz8/f7r1aoefrz/z//zw0EL5Tv5jw/u/ltkFHaGhoVhafn4C5r+awk4UNya50ialdK4MxHJ2YP+568zZfIi4MZ1oUCqPoV5MJwdm9GzA68Agrtx7xM5TV/ELeP2RlsNUzJ+JmRsOMGjeRvo1Lk+y+K4cvnSLZXtOARAQ+G5Y1B2PJ/y98zi/tq2ObQQTyN9ysrdjTp8m3PJ4QlBwMKkTxcPaypK/dx4nMCiIJmXzcePhY0b9vZU7j56SN31yfm5cHmcHuw+2Keahep3KYf/BXrPV9HNrV6Jh09r8PulPbt+8a3Ss36Ce3L19n8ULVn60jXWrNrN350FSpU3BvTsPeOz1BHsHe/oN7snYEZN59dKXHj92oE6D6rx65cuEX6exdePHVz4S8zJ92p+sXbMZN7f41KxVGSsrK2xsP7wYhqm6dOpr9HrJ4jVMmjKClq0aMn3qnxw/fgaAfXsPkylDETJmTIu7uycPHrhjYWHBmHGDmDp5NvfuPaR1m8Z07NQCCwsLpk39kz/n/P3N+imR58SxM7Q51sPwetvm3WxYu41dB9fw88CeNKrTLsLznJzDhlLFieNK+5a9WLt6MwDr125lz6F19Ojd4YNBR5Xq5bCzs2Xle0Or3vp96lyWL11HsuRJuHHtFi9eeJMgYTy69WxHi8ZdsLa2YtDwfpSvWAovz8cM/PlXjh89/ZWfgkjkMbvhVVeuXKFChQqfVbdChQpcuXIlkntkfrYcu8SwvzYzqFklahfLQelc6RncojJVC2Zl4so9PH/vaYmNtRUFMqWkWPa0tKtShH6NyjF4/ib2nQ0/J+N9cWM6M6lLHQKDgun42xIq9/udiSt281ODsKd4b5flBRizdAfZUyehTO4Mn+y7paUFqRPFJX3SBFhbWfLMx5cZ6w7Qs25pALpNWU7axPGY2LkOHk+9+XWxaZP55PtzdHKgXMUS7Nt1kOcmjp/PVyAXYycPYc/OA4wZPtnoWM482ahVvypDfxnzWQ8XXrzw5vSJczz2egJA555teOL1lGWL1lC/SU2atKjHj90HMWfGX0ybM47kKZN+okUxJ9eu3mTP7oMs/ns19eq0wdnZkWXL/4jUa06ZPBuAEiULG5W/euXLiRNnefAgbOn2Jk3rkiBBXCaMn0GJkoUZNqIvgwaOYUD/Xxkx6ucIl+KV6OH2rbts3bSLwkXzf/CBqL9f2BzJ169fs37tuwcvoaGhrF29mcRJ3EicJOK5irXrVeXp0+fs2r4/wuOPvZ5w6sRZwzCq/kN6s3/fYQ7sO0LPPp0oWqwA7Vv9wOaNO1m4bAYxYrp8zdsVU4WERN1fNGR2QcfChQupUqUKHTp0+Gi99u3bU7VqVRYuXPidemY+lu05RfqkCUgQ23gSZYnsafF/HciVu48+eG6ONEmIF9OZTUcvfvI6udMlY8OojiwZ0Iq5PzVl25guZEuVCIDkCWIDcOzybQ5euEnj0nl48Pi54S84OISAwCAePH7OS7+Il0AFmL52HxmTJ6RUznScv/mQxy9e0qNOKTKncKNjtaJsPX6ZkJD/ZjYruihfqXTYqlUrPr5q1b9lzJyOOX9P4Z/L1+nQole4pWx/HtyLY4dPce/OA5IkTUSSpImIHScWAPETxCNR4oQfbDtJ0kS069SMwf1+JTQ0lOq1KrJo/nIO7T/GskVrOHX8LNVqVTT5vYr5WLN6M7nzZCdN2pSRdo0H98OCCtfYsT5Yx8XFmYGDf2DwwDH4+vpRp25V1q7ZzMYN29m0cQdr12ymXv0P72kj5u/hAw/s7GxxdHKI8PizZy/w8/Pn2dMX4VbTfPsQJKJFDxIncSN/wdysX7OFoKCgcMf/LVee7FSpVo7Bv4wBoGadSkybPIeTx88wecIsfLxfUrZ8CRPfncj3Y3bDq6ZPn06tWrWYOnUqNWvW5K+//jJaMjdbtmw0bdqUMmXKcODAAaZPnx7VXf7unni/MloF6q3A4LAvu+BPRMABgUH4fCQQeJ+VpSUZkr1bY/zI5dsA5M+YAgD3p2FPX3r9Hn6ZU8/nPlTu9zu965emSZl84Y7/c+8Raw6eY3H/sI23vF74EMPR3rDHR7xYzgQGBfPspW+4lbrEfNSoG7YfxvbNez77nOQpkvDX8hk89npC8/qd8H3lF65OoiQJSZosMYfOhh+y9efisAmUWVMWDncMoP+wH9i+ZY9hqEF8t/g88vAyHH/k4UlCt/if3V8xP29XJ4sZI/Ke7KZIGbZox+PHTz9Y56d+Xblz+55hqV03t/icPftu40p3d0+yZcsYaX2UyJc8RRL8/PyN5ly8LzQ0lIvnr5AjVxZsbGwIDAw0HHv7PfMkgnuoZu3KWFpaGjYD/JQRo39m9oy/uHM7bFGMBAnj4/HePjIe7p5Ge4KImBuzCzqCg4OpVKkSv/32G61ataJ06dJGxy0sLAgODmbOnDn07NnzP7nRV/IEsTl86RZ3PJ6QPOG7CeFbjl3C0sKCtEniv5m3YWE0DApgx8krePv6Gy1LGxgUzH2v5zg72BEv1ocncj/18WXeliOkSxKfAhnDni7my5CcCZ1qh6s77K/NuMWOQZvKhUn7Zi+RfxuzZAc1i2Q37DUSO4YTz1768uKVHzGdHLjp/gRrK0tiOUf8dEmiXuw4rhQpnp91Kzcbhhi8L1HihDg4OnDjvV1048WPw8KVswgJCaFpnQ48jWDFKoC+PYfg4GD8775w0Xy0bN+YYQPGcuPqrQjPK1gkLyXLFKVk/mqGsseeT0j93hPxNOlSsfXNMr1i3uLGi2N4WvyWtbU1DRrVxPfN7uQQtvt4jBgu3Lp597OeGr/PxcWZgIDXvH5tPN/txx87A7Bzx74Iz0uTJiXt2jejYrn6hjJPz8ekS5fa8Dp9+tQ8evTYpP5I1IgTx5Un//o+ypQlPeUqlmTXjv2GYZ6Jk7jh4GBvtDv42tWbyZMvB/Ua1mDRguVA2EaRtepW4Z/L140eerxVs25l7t97yNHDJz/ZtwaNa5IosRsTx79b2fOx1xPSpEvF3t2HsLa2JmWqZHh6hr+ORKJotHStra0tQ4cOpWnTpri6unLu3Dn69+/Pjh07Pnlu6dKl+eWXX8iaNSvW1tZcvXqVKVOmmDzayOyCDgA/Pz86dOjA0KFDqVSpEhkzZsTFxQUfHx8uX77M5s2befDgQVR3M8o0L5+fgxdu0HLMQhqUyk1MJwf2nbvOwQs3qVk0O/FjuXDl7iM6/LaYcnkykjJhHCwsLbh0251NRy+SKE5MGpV+N9nc87kPNQfOomrBrAxrVcVQ3nrsQrKlSkzS+K48efGKlfvP4Ov/msld62JpGbZLvFucmLjFiRmuj2OX7iBODCdK5UwX4XvYduIy1+57Mr5jTUNZ9lSJie3iRJ8ZqymVKz0Lth6lVM70WJmwsIB8X1VrVsDGxuaDQ6t++30kBYvkJVnsrIayBctnkDxlUn6f9Cd5C+Qib4FchmOPvZ6wf0/YBn4RbeT3drzy0YMnOHfmUrjjlpaWDBr5EzOnzuPhg3fLOm9at52fB/fk6eOnJE6aiAyZ0tK9fd9w54v5mTR5OC4xXDh04BgPH3qQIEE86tWvTvoMaejXdwSvXoU9fR48pA+Nm9QhS8aiht3BAfq8CRwyZgr7LmrQsCYFC4Z9/40dMw2A7Dky8+e8SaxYvp6bN+5g72BH1arlKVgoD3/O+ZuzZyIejjpqdH9WrdzAyZPnDGVrV29h8bKZDBzcG4CKlUpTr06bb/ypSGSYOXcC/n4BHD92msdeT0mXITVNm9fFz8+fEYMnGOpNmfErhYrkI2Gsdxmsv+YupXHTOowa15/UaZLz4L47depXI0nSRDRrEH71vAwZ05I5SwYmT4h4KfD3OTk70m9AT0YN+80o27Jh7VZ6/dgJS0tL8uXPiZ2dHTu3RRwgi8ybN486deowceJErl27RosWLdi0aRMlS5bk4MGDHzyvatWqrFmzhsOHDzN48GBCQ0OpV68ef/31F3HjxmXixImf3QezDDreevjwIbNnz47qbpid3OmSMa9vM2as28+yPad4/tKPxHFj0aVGcVpUCJuwmMDVhdK50nP8yh3WHz5PUHAIbrFjUL9kbtpUKkQs509vXJQxWUK2n7yC5zMfnBzsKJAxBZ1rFCNJPNev6r//60B+W7GbjtWKGvXD1saa3zrXZvhfW5iyag950iejb6NyX3UtiVw16lbGy/MJB/Yc+exzMmcNW3CgY/dW4Y4dPnDcEHR8icYt6hLLNSbTJ/1pVL5w7jKSJk9Mm07N8PX1o3eXAVy9ol18o4NVKzfStHk9WrdtTOzYsXjp84rTZy4wcMBoNm/6dLZqwKAfjF43a17P8M9vg457dx9w6OBxqlQtR4IE8QgJCeGff27Qvesvhp3G/61c+RIUKpyXXDmMs/Fbtuxi6JDxtO/QDAsLCwYPGsv2bXtNfdsSBTZv3EntulVo37kFLi5OPHn8jI0bdjD+12ncvnX3o+f6+wdQp1oLBgztTYMmtXF0dODi+Ss0qdeBPbvC/6CrVTfsAd+qFZ8eWtWrTyfcH3qwZNFqo/Kxo6YSJ25sfvipE56PHtOmefdwmRqJZNFkQnfevHlp2LAhvXv3Zvz48QAsWLCACxcuMGbMGAoXjnioMkCXLl1wd3enVKlShmzwzJkzuXLlCi1atDAp6LAAzGqW7tmzZ02qHxoaSo4cOUy+ju/euSafI/Il0tcYH9VdkP+I5wER72Ui8q052mgpc/k+Hr0w31VKfddG3V5xjtX7fHbd0aNH06tXL2LHjo2Pz7t91vr27cuoUaNImjQp9+/fj/Dcw4cP4+zsTNasWcOVAxQsWPCz+2F2mY6nT59+1vKYCRMmJH369P/ZfTpERERERD4lZ86cXL161SjgADh27BgAOXLk+GDQsWfPHvr27cvQoUOZP38+oaGhNGrUiDx58lCvXr0Iz/kQsws6SpYs+dHjCRIk4KeffqJ9+/YEBwfz119/faeeiYiIiIi8EYUTyW1tbbGzM844BgQEhFsQA8DNzQ13d/dw5W/LEiVK9MHrDBs2jJQpU/LLL78wYMAAAF69ekXt2rVZt26dSX2ONjN048ePz4QJE7hx4wadO3dmyZIlZMiQgdatW0d110REREREvpt+/frh7e1t9NevX78I6zo4OBAQEH6rBH9/f8PxDwkICODq1ausWLGCBg0a0LhxY06cOMHChQvJnz+/SX02u0zHv73NbLRr1w4bGxsWLlzI8OHDuXUr4uUyRUREREQiXRROJB81ahQTJkwwKososICwVWH/nRUBsLe3Nxz/kKlTp1KgQAFy5cplmNKwbNkyLl68yKRJkyhQoMBn99lsMx0JEiTgt99+M8pspE+fntatWyvgEBEREZH/rNevX+Pj42P0F9HQKggbRuXm5hau/G3Zw4cPIzzPxsaG1q1bs3HjRqM51EFBQWzevJk8efJgY2MT4bkRMbtMR8KECenbty9t27bF2tqaBQsWMGLECG7fvh3VXRMRERERCRNNNgc8c+YMJUuWNOx599bb4VFnzpyJ8Lw4ceJgY2ODlZVVuGNvy62srAgMDPysfpjdkrmvXr3Czs6OM2fOMHLkyM/Kapw+fdrk62jJXPletGSufC9aMle+Fy2ZK9+LWS+Zu3JElF3bsfYvn103X758HD161GifDltbWy5cuMCTJ08My94mTZoUR0dH/vnnHyBsw93Hjx/j6elJ1qxZDcGFk5MTly9f5uXLl2TKlOmz+2F2mY6348ty5szJsmXLPlrXwsKC0NBQrK3N7m2IiIiIiES5Y8eOsWzZMkaNGkX8+PG5fv06zZs3J0WKFEYLMi1YsIASJUpgYWEBQEhICOPGjWPEiBEcOXKEBQsWYGVlRevWrUmaNCmNGzc2qR9m92u9ZcuWUd0FEREREZGPiyY7kgM0a9aMYcOG0bRpU1xdXTl37hxVqlRh//79Hz3v7aij7t27M2jQIOzs7Dh37hy1a9dm1apVJvXB7IZXfS8aXiXfi4ZXyfei4VXyvWh4lXwvZj28avmwKLu2Y90BUXbtL2V2mQ4REREREbMXjTId5sBsl8wVEREREZH/Dwo6REREREQkUml4lYiIiIiIqUL/k9Oiv5gyHSIiIiIiEqmU6RARERERMZUmkptEmQ4REREREYlUynSIiIiIiJhKmQ6TKNMhIiIiIiKRSkGHiIiIiIhEKg2vEhERERExVaiGV5lCmQ4REREREYlUynSIiIiIiJhKE8lNokyHiIiIiIhEKgUdIiIiIiISqTS8SkRERETEVKGhUd2DaEWZDhERERERiVTKdIiIiIiImEoTyU2iTIeIiIiIiEQqZTpEREREREylTIdJlOkQEREREZFIpaBDREREREQilYZXiYiIiIiYKlTDq0yhTIeIiIiIiEQqZTpEREREREwUGqLNAU2hTIeIiIiIiEQqBR0iIiIiIhKpNLxKRERERMRU2qfDJMp0iIiIiIhIpFKmQ0RERETEVFoy1yTKdIiIiIiISKRSpkNERERExFRaMtckynSIiIiIiEikUtAhIiIiIiKRSsOrRERERERMpSVzTaJMh4iIiIiIRCplOkRERERETKVMh0mU6RARERERkUiloENERERERCKVhleJiIiIiJgqVPt0mEKZDhERERERiVTKdIiIiIiImEoTyU2iTIeIiIiIiEQqBR0iIiIiIhKpNLxKRERERMRUIZpIbgplOkREREREJFIp0yEiIiIiYqpQTSQ3hTIdIiIiIiISqZTpEBERERExleZ0mESZDhERERERiVQKOkREREREJFL9Z4dX5awzLaq7IP8R1/ZNiOouyH+Ec64WUd0F+Y/wD3od1V0QiXKh2pHcJMp0iIiIiIhIpPrPZjpERERERL6YJpKbRJkOERERERGJVAo6REREREQkUml4lYiIiIiIqbQjuUmU6RARERERkUilTIeIiIiIiKk0kdwkynSIiIiIiEikUqZDRERERMRU2hzQJMp0iIiIiIhIpFLQISIiIiIikUrDq0RERERETKWJ5CZRpkNERERERCKVMh0iIiIiIqbS5oAmUaZDREREREQilYIOERERERGJVBpeJSIiIiJiKk0kN4kyHSIiIiIiEqmU6RARERERMVGodiQ3iTIdIiIiIiISqZTpEBERERExleZ0mESZDhERERERiVQKOkREREREJFJpeJWIiIiIiKk0vMokynSIiIiIiEikUqZDRERERMRUoVoy1xTKdIiIiIiISKRS0CEiIiIiIpFKw6tEREREREylieQmUaZDREREREQilTIdIiIiIiImClWmwyTKdIiIiIiISKRSpkNERERExFTKdJhEmQ4REREREYlUCjpERERERCRSaXiViIiIiIipQrQjuSmU6RARERERkUilTIeIiIiIiKk0kdwkynSIiIiIiEikUtAhIiIiIiKRSsOrRERERERMpeFVJlGmQ0REREREIpUyHSIiIiIiJgoNVabDFMp0iIiIiIhIpFKmQ0RERETEVJrTYRJlOkREREREJFIp6BARERERkUil4VUiIiIiIqbS8CqTmF2mY9q0aeTOndvw2tramrp16xI3btxwdUuXLs3OnTu/Z/dERERERMREZhd0dOjQgXTp0hlex4gRg8WLF5M1a9ZwdRMkSEDx4sW/Z/dERERERAgNCY2yv+jI7IKOiFhYWER1F0RERERE5AtFi6BDRERERESiL00kFxERERExVTQd5hRVlOkQEREREZFIZZaZjmbNmlGgQAEA7O3tCQ0NpUuXLtSoUcOo3vsTzkVEREREvpuQqO5A9GKWQUe5cuUoV66cUdm/A463QkOV2hIRERERMWdmF3RYWVlFdRdERERERD4qui5dG1XMbk5HrFixoroLIiIiIiLyDZld0OHh4cGqVauoXbs2tra2Ud2daCVfoVxc8Twe4V/23FkM9dp3b8GSTX9y6NI2zt49wJYjK+k3rBeucWJ9tP0qtStwxfM4J2/tDXcsa85MDBz9Eyu3L+D8g8Nc8Tz+wXYatKjNxNmj2HVqPVc8jzNq8qAvfs/y/c1asYVstbtQs8cIo/KQkBCWbd1P3R9Gkb9xL0q06kfH4dM5c+XmF7X3VmBgEH+s3Eq1rsPI06AHJVr1o/OI3/F48uy7tinmI1OmdCxePJN/rhzixfPruD88z66dK6lcuWy4up06tuDcuT289LnJ7VsnGDtmEI6ODuHqWVhY8MMPHbn6z2F8vG9w6uR26tevHuH1LSwsaN+uGSeOb8P7xXU83C+wbesysmXL9M3fq0StYsUK8jrgfoR/+fLlMqpboEBudu9axfNn17h75xQTJgzFyckxXJs5c2Zl/fqFPPa6zJPHV9i4cRHZI7h3fvqxC/v3rePB/bN4v7jOxYv7GTduMHHjxo609ysSmcxueNWKFSuoVq0a1apVw8fHh1WrVrFo0SJ27doV1V2LNhbMWsL5M5eMyu7cumf458zZM3Ll4lU2rdnOq5evSJ0uJXWb1KB42cLULNUYP1//cG06OjnQZ2BXXr3yjfCaxcsUpk7j6ly9dI37dx6QMk3yD/avbZdmODk7cu70JeIliPuF71KigseTZ8xetQ0H+/APBCYsWMOC9buoUiwv9csXxdvXjxXbDtJq4ETmj+hF1rQpTGoPIDAomM4jf+fMP7eoXaYQ6ZInxvuVL+ev3ublK3+IY1ofv7RNMS/JkiXBxdmJvxYux/3hIxwdHahZsxJrVs+jY8cfmT1nEQAjR/5Mn96dWbFyA1OnzCFjxrR07tySTJnSUblKY6M2hw37iZ9+7Mrs2Qs5ceIsVauWZ+Ff0wkNDWXZsnVGdWf/MYGGDWuycOEKpk+fi5OTIzlyZCFePN08/6+mTJ3DyRNnjcpu3Lhl+Ofs2TKxdctSrly5Rp8fh5AksRs9e7YnTZqUVKvW1FAvR44s7Nm9mvv3HzJ8xG9YWlrSvn0zduxYQeEiVbh69d1Dmly5snH23EWWLV+Lj88rMmRIQ+tWjahYsTR585bD19cv8t+4fJyGV5nE7IKOJk2aYG9vT40aNWjUqBGNGzemefPmPHr0iMWLF/P3339z6tSpqO6mWTt55DRbN3w4SOvW6qdwZWdOnGPyn2MoWa4om9ZsD3e8Q8/WvHrpy9GDJyldsXi444vnreSPKQsI8A9gwKg+Hw06mtZoz8P7HmF9jSBrIuZrwvzVZEuXgpCQEJ55vzKUBwUHs2zrfsoWzMnI7s0N5eUK5qRSp8Fs3HciwqDjQ+299deGXZy4dJ35w3tGeL4pffyaNsW8bNmyiy1bjL/jpk2fy7GjW+jeox2z5ywiYcL49OjejoULV9CyVXdDvWvXbjJp0ggqVy7Lxo1h33WJEiWkZ4/2TJ8+l+49+gMw58+/2bVzJb+OGsCKFRsICQlbpqZOnao0a1aPOnVbs3btlu/0jiWqHTxwjFWrN37w+NBhfXn27DllytbFx+clALfv3GfmjLGUKVOMHTv2ATB4cB/8/PwpWqwaT58+B+Dvv1dx8cI+hg3tS/0G7Qxtvv/Pbx09coqlS2dRpXJZli1fF+64iDkzu+FVAP7+/ixZsoRq1aqRMGFCOnXqxLVr1+jRowfHjh3j8uXL/PLLL6RMmTKqu2q2nJwcTZqU/+CuOwAuMV3CHUueMikt2jfk14G/ERwUHOH5T7yeEuAf8FnXehtwSPRy4uJ1th8+w48ta4c7FhQUjP/rQOL86/6JHdMFS0sL7G1tTGoPwoZr/b1xD6XzZSdr2hQEBQfjF/D6i/v4pW1K9BASEsK9+w+JFTMGEDbUxcbGhqXL1hrVe/u6fr1qhrJqVctja2vLjJnzjerOnLWApEkTUbBAbkNZj+7tOHbsFGvXbsHCwiLCoVry/8nZ2SnC/666uDhTpnRRFi9ebQg4ABYuXIGPz0vq1K5qKCtSOB+7du03BBwAHh6e7Nt/hEqVSkc4HOt9t++EjVqIGSvGV74b+SZCovAvGjLLoON9z58/Z9asWZQoUYJkyZLRt29ffH19GTp0KNeuXePgwYNR3UWzM3LyQE7e2svZeweYv+p3smTPGGG9WLFjEjd+HHLnz8EvI3sTFBTEsYMnw9XrN7wXRw+eZN/OQ5HddTFTwcEh/DpnObXKFCRd8sThjtvb2ZI1bQrW7jnCxn3Hcfd6ytXbDxgwdSExnBypU7awSe0B3LjvgefTF6RNnoghv/9N/kY/kL9RL2r3HMmx81dN7uOXtCnmzdHRgThxXEmVKjndu7WlQvmS7N59AAA7u7DhdX5+xsNF3w5JyZkrm6EsR47MvHz5isuXrxnVPX78zJvjYXPiXFycyZs3BydOnGXYsL48eXyFF8+v88+VQ9SpUxX5//XHH+N5+uQffLxvsG3rMnK9d/9kyZIBGxsbTp40Hn4VGBjI2bMXyZEjs6HMzs423D0J4Ofrh52dHZkzZwh3LE4cVxIkiEfhwvn4bcJQgoKC2Lfv8Dd8dyLfh9kNr/qYhw8fMm7cOLZs2cLQoUOpXr06+fPnj+pumY3AwCC2rt/J3h0Hefb0BWnSpaRVpyYsXDeLhpVbc/nCux9VcePH4cCFd0MD3B88oneHAdy6fseozeJlClO4RAFqlGz03d6HmJ/l2/bj7vWUWYO6fLDOqO7N6TPhT/pNeve0OEmCuMwf0YskCY3n7nxOe3fdvQBYuGE3MZ0dGdi+AQCzV22l4/DpLB7dh3Qp3gUXkdGmmLexYwbRrl3YePng4GBWr9lMt+5hw6Ou/nMDgEKF8rJ377sHJkWKhP03I3GihIayhAkT8OiRV7j23d0fAeCWKAEAqVMnx9LSknr1qhMUFES/fiN44e1N1y6tWbRwOt7ePmzbtufbv1GJMq9fv2bVqo1s3rKLJ0+ekjFDOnr2bM/uXasoXrw6Z85exC1h2P3h7uEZ7nwPD08KF85neH316k3y58+FpaWlYciejY0NefPmBIzvS4AECeJx7+5pw+t79x7SrFkX/nlzf4tEJ9Em6EiaNCmNGjWiYcOGZMmSBQsLCw4dOsSiRYuiumtm4/Txc5w+fs7wevfWfWzdsJO1uxfTq38X2jboZjj24tkLWtbpjJ2dLRmzpqds5ZI4OhkPE7CxsabfsJ4snb+SG1dvIf9Nz31eMm3JRtrVrUDsCIbfveXkYEfqpG5kT5eS/NnS8/iZN3+u3kaP0bOYN7wnrjGcTWrP1y9suN4rvwCWjetLwriuAOTLmo7KXYYwd+0ORr2ZPxIZbYr5mzzlD1au2kiiRAmoU7sqVlaW2L4Zynf6zAWOHj1Fn96dePjAnT17D5EhQ1qmThnF69evcXCwN7Tj4GBPwOvww+z83wwZdbAP+250cnICIG7c2BQuXIVjx8N+DK5fv41rV4/wc7/uCjr+zxw5cpIGR9obXm/YsJ1Vqzdy8sR2hg3vR9WqTQz3UkAEQzX9/QOM7rUZM+czbeqvzJo5jvHjf8fS0pJ+/brh5hYfwKguwNOnz6lQsQH29nbkyJ6FGjUq4uTsFBlvVb6A9ukwjVkPr4oTJw4dO3Zk//793Lp1i5EjR2JjY8PAgQNJlSoVRYsWZcaMGVHdTbN299Z9dm3ZS/7CubG0fPevOzAwiMP7jrFn+wF+nzCHYX3HMHLSQEqULWKo07xDI2LFjsWUMbOioutiJqb8vYGYzk40imABgbeCgoNpO2QKLo72/Ny2HqXzZ6d+haLMGtSVe48eM2/tDpPaA7CzC/vxmCNDKkNwAOAWLzY5M6Q2Woo3MtoU8/fPPzfYtWs/CxeuoEbN5jg7ObFm9TzD8Xr123Lu3CVmz/6N69eOsmb1PFasXM+ZMxd4+fLdIgN+fv7YRbBEu729Xdhx/7AhWf5vhsXcvHnHEHAAvHrly8aN28mbN4c2uP0PuHHjNuvXb6VE8YJYWloahku9HdL3Pnt7O6PhVH/8sZBff51MgwY1OHt2N6dP7yRVquSMH/87AC9fGS9+ERgYyK5dB9i0aScjR02iW/df+GPWeCpVKh2J71D+H9na2vLrr7/y4MEDfH19OXLkCGXKlPns8+vVq8ehQ4d4+fIlz5494+DBg5QsWdKkPphd0OHo6Ejjxo3ZuHEjDx48YOrUqaRMmZKJEyeSJ08eMmfOzMiRI7lz586nGxMA3B8+wtbOFoePTHg8ffwcnh5eVK1dAQBnFyc69mzF8oVrcHZxInFSNxIndcPRyQELLEic1I3Y7/1ok/9Pdx56snLHQRpVLo7nsxc88HzCA88nBLwOIig4mAeeT3jh84qTl65z/a47JfJmNTo/eaL4pEqc0PBj/nPbA4jvGhMg3OT0sDJnvN8s3xwZbUr0tGrVRvLmzUm6dKkBePjQgxIla5IxUxFKlKxJipR56NdvBEmSJOLatXcBpofHIxImjB+uPTe3N8NmHoYNs3r4ZriVp+fjcHU9vR5ja2v7yYnA8v/h/n137OzscHJyxN3jzTC8CO6hhAnjG4bpvTVw0BiSJM1JiZI1yZWrDIUKVzE8FLx29eMPPo4cOcnDhx40bFDzG70T+SrRaCL5vHnz6NWrF4sWLaJ79+4EBwezadMmChcu/MlzBw0axOLFi7l37x69evWif//+nDt3jsSJTRuObHbDqzw9PbG3t+fly5f8/fffhj06QkOVwvpSSZMnxt/PH99P/KCytbPF+c0QmJixYuDk7ETbrs1p2zX8cJOdJ9exY/MeujTvEyl9FvPg+fQ5ISGh/DpnBb/OWRHueMWOg2hcuYRh6dngCFLNQcHBBAWHmNTeT63qkDZ5IqytrfB8b5WXd/16Qew392pktCnR09uhKTFjGAeV16/f4vr1sCGiGTOmJVGihCz4a7nh+NmzF2ndujEZM6Y1mkyeL1/YOPszZy8CYXM83N0fkehf4+4BErklxM/Pz2j1Ivn/lTJlMvz8/Hn58hUXL/5DYGAguXNnZ8XKDYY6NjY2ZM+emRUrNoQ7//nzFxw69G4T3VKlinLv3kOu/HP9k9e2t7cnRkytXiWfL2/evDRs2JDevXszfvx4ABYsWMCFCxcYM2bMRwOP/PnzM3DgQH744QcmTpz4Vf0wu6Bjx44dLFq0iHXr1hEQ8OElWK2tralcuTJNmjShbt2637GH5ss1TiyePXluVJY+c1pKli/G/p2HCA0NxcHRntDQUPz9jD/bclVKEss1JhfOXgbgyeOndG7eO9w1mrapT448WfmhQ3+8HoV/2if/X9IkS8TEH9uGK5+yeAO+fv781KoOSRLGJejNUspbDpykSM53O+teunmP2w8fUbtMYZPaA3BysKdozszsO3mBW/c9SJkk7IfezfsenP3nFnXKRV6bYt7ixYuDl9cTozJra2uaNKmDr68fly5HvBKZhYUFo0b259UrX2bNWmAoX7d+G+PGDaZD++aGfToA2rVtyv377hw+fMJQtnz5erp1a0Pp0kXZuXM/ELa6UNWq5di9+5AekP2fiRs3No8fPzUqy5Y1I1WqlGXr1t2Ehobi7e3Dzl0HaNiwJiNGTjQM3WvcuDYuLs6sXBU+6Hhf3TpVyZs3Bz/+NNRw/zg6OhAaGhpupauaNSoRO3YsTp08F1FTIhGqU6cOQUFBzJr1brh8QEAAc+bMYdSoUSRJkoT79+9HeG6PHj3w8PBg0qRJQNjctlevwu+B9TnMLuioUaPGR48XK1aMxo0bU7t2bVxdXb/4jf8/+m3WSPz9Azh9/BxPHz8jdbqU1GtaE38/f8YPnwpA8lTJmLt8GpvWbufWtduEhIaSJXtGqtapyP07D/hr1hIA/P0C2Lk5/MZ9ZSqWIGuuzOGOJUqSkGp1KwFhO54DdOjZCoCH991Zt3yzoW7JckVJnzktADbW1qTLlMZQd9fWfVy99OknPfJ9uMZwplT+7OHKF27cDWB0rGD2DKzbc5RXfv4UzJ4Br2feLN68FztbG5pUKWlyewDdGlfl6Pl/aDN4Mo0qlQDg7017iOHsSJta5SOtTTFv06ePJoaLC/sPHOHhAw8SJIxPw4Y1yZghLX36DOHVm6zuhPFDsLe35+zZi9jYWNOgQU3y5s1Bq9Y9uHfvoaG9Bw/cmTxlNr1/6ISNjQ0nTpyhWrUKFC1agKbNOhtWGQIYPWYKdepUYdnSP5g4aRbeL3xo264pNjY2DBj463f/LCRyLVr4O37+/hw5fAJPrydkzJiWNq0b4+vrxy/9RxnqDRw4mn1717Bzxwpmz1lEksRu9OjRnm3b9xgtLlCkSH5++aUHO3bs48mTZ+TPl4vmzeuxZetupkyZY6iXNk1KNm9ewvIV6/jnnxuEhISQO1c2GjWqxa3bd5kydfb3/BjkA6JyIrmtrS12dnZGZQEBAbyOYFGMnDlzcvXqVXx8fIzKjx07BkCOHDk+GHSULl2aQ4cO0a1bN/r370/cuHFxd3dnxIgRTJs2zaQ+m13QEZGsWbPSuHFjGjZsSOLEifHw8GDFihWsW7eOnTt3RnX3zMbOzXupUrsCLTs0wsnFmWdPnrF9026mjfuDu7fCbiaPh4/YtnEXBYrkoUb9ythYW/PwvjuL5ixjxsQ/ef7sxRddO0myRPTo19Go7O3rYwdPGgUd5aqUomaDKobXmbNlIHO2sLXJHz30VNARTU36qR3z1+1ky4GTHDx9CRtra3JmTE2XhlVImTjBF7WZOqkbfw7rwcS/1jBr5RYsLSzJlyUdvZrVIEGcWGbTpnxfy5evo2WLhrRv14w4cVzx8XnJqVPn+fnnEWzYsN1Q78yZi3Tr1oaGDWsSEhLC8eNnKFe+vtESum/9/PNInj17Qds2TWjWrC7Xrt+iWfMuLFmyxqiep+djSpSsyejRA+jerS02NjYcOXKSFs27cu7cpch+6/KdrVu/lYYNatK9eztixHDGy+sJa9ZsZviI37hx47ah3pkzF6hQsSEjR/zMuLGD8fF5ydx5S+j/XmACYfOMgoOD6dWzAy4uTty+fY9Bg8YycdIsgoPfbb57/4E7q9dsomSJwjRtUhcbG2vu3n3A9N/n8euvk402F5T/pn79+jF48GCjssGDBzNkyJBwdd3c3HB3dw9X/rYsUaJEEV4jVqxYxIsXj8KFC1OqVCmGDBnC3bt3admyJVOnTiUwMNAoe/IpFoBZ5oLfLpHbuHFjMmXKhJeXF7t376ZevXrUrVuX1atXf1X76ePl+UY9Ffm4s7tGRnUX5D/COVeLqO6C/EdYWFhEdRfkPyLw9YOo7sIHPalWLMqu7bblyGdnOq5fv84///xD5cqVjcpTpkzJzZs36dGjh2H41PuSJEnCvXv3AKhfvz7Lli0Dwv7/f/78eWLEiEGyZMk+u89mt3pVu3bt2Lt3L7du3eLHH3/k6NGjlC9fnkSJEjFgwAB90YmIiIjIf9rr16/x8fEx+oso4ADw8/MLF6BA2KIEb49/6Ly311qx4t1CLaGhoSxdupSkSZOSNGnSz+6z2Q2v+v3337l16xa1a9dm48aNBAUFGY5pgp6IiIiImIPQL1i6Niq4u7tHuLytm5sbAA8fPgx3DODp06f4+fnx/Plzo7ltELbaLICrq6shG/IpZpfpOHHiBClTpuT3339n/PjxFChQIKq7JCIiIiISLZ05c4Z06dLh4mK8nHj+/PkNxyMSGhrKmTNniBcvHjY2NkbH3s4D8fLy+ux+mF3QkT9/ftKlS8esWbOoUKECBw8e5NatW4waNYocOXJ8UZu2tra4uLgY/dnY2nz6RBERERGRaGzFihVYW1vTrl07Q5mtrS0tW7bkyJEjhpWrkiZNSvr06Y3OXbp0KdbW1jRv/m7PNjs7Oxo3bszFixcjnKD+IWYXdADcuHGDwYMHkz59egoWLMj69etp2bIly5cvJzQ0lDp16lCwYMHPbq9fv354e3sb/bXv3iLy3oCIiIiI/H+LJjuSHzt2jGXLljFq1ChGjx5N27Zt2bVrFylSpODHH3801FuwYAFXrlwxOnfmzJlcuHCBadOmMWbMGLp06cK+fftInjw5vXuH38/tY8x29ap/s7S0pHz58jRq1Ijq1avj6OjIkydP2LhxI61atfrouRGtZZw8RhYCXwdGZpdFAK1eJd+PVq+S70WLusj3Ys6rVz2uFHWrV8XdtM+k+nZ2dgwbNowmTZrg6urKuXPnGDBgANu2bTPU2b17NyVKlAj3/+948eIxZswYqlatipOTE2fOnGHQoEFG536OaBN0vM/BwYFatWrRuHFjSpUqZZh9b4r/1yVzM2fLQI+fO5Ezb1YsLCw4c+I8Y4dO4cqFiHfofd/OE2tJnCzitZpv37xLhQK1jcrixItNt5/aU6JsEWK5xuSx5xMO7z9O/57DDXVy5stG/5F9SJEqKRfPXmFQn1Hcun7HqJ1fRvxAyjTJaVO/2xe8Y/MX3YOOOw89mbZkA6cu38T75SsSxo1NpaJ5aF69NA52tgCEhISwYvtBlm87wF0PLxzs7MiYKint61QgR4ZUn7xGttpdIizv3rgarWuVMyrbfOAEc9fs4OZ9D5wc7CmRJys9mlbHNYazoc7rwEDGz1/NloOnsLG2pl75IrSrU8GoHY8nz6jebRgzBnQmZ4bUpn4sZun/NeiwtbVl8ODeNG5UG1fXmJw/f5mBg8YYdgT/kGtXj5AiRcQrq1y7dotMmYsYXrdv14wSJQuTL29OkiVLzIIFy2jdpme48zJmTMv0aaPJnj0zV6/eoEePARw5etKoTo/u7WjRsgG5c5c12nvh/8n/a9Bha2vL4EG9adSoFq6usTh//jKDBn/6XgOoV7caP/zQkYwZ0+Lj84oNG7bz8y8jePLkmaGOvb09kyYOJ1++HCRJkggrKytu3rzDvPlLmTFjvtHiORkzpGXatF/f3Ws9B3D06Cmja3bv3paWLRqQO0+5/9t7zZyDDq8KURd0xNtiWtBhDsxu9ar9+/ezf/9+Dh48yMGDB3n+/Hm4On5+fixatIhFixYRN27c799JM5Upa3oWrf8Dj4ePmDZuNhaWFjRqUYe/1sykXvkW3Lpx56PnjxwwAUcnR6OyREkS0vPnThzcc9SoPGGiBPy9IWxH1CXzV/HI3ZP4CeORLWdmQx1nFyemzx/P2ZPnWfbXamrWr8LkP8dQvURDwyoIadKnom6TGtQu2+xbfATyjXk8fkajvmNxcXSgYcVixHR25OzVW0xfupFLN+8yuW97ACYsWMOC9buoUiwv9csXxdvXjxXbDtJq4ETmj+hF1rQpPnmtgtkzULV4PqOyDCmNfzAu3bKfEX8sJX/W9PRuUYtHT56zaOMeLt64y6Jfe2P3Zq7WvLU7Wb/3GG1ql8fXL4AZyzeTJEFcKhV997Bhwvw1lMiT9f8m4Ph/NmfOb9SuVZnJk2dz/fotmjWrx/p1f1G2bF0OHjr+wfN+6D0IZ2cno7JkyZIwbOhP7Nix16i8d+9OuLg4c/z4adzc4kfYnqWlJcuWzebZ0+f07TucKlXKsnLln2TIWBgfn5cAxIsXh19+6UHDRh3+b38E/j+bM3sCtWpVZvKUOWH3WtO6rFu7gLLl6nHoI/dau3ZNmTplFDt37afPj0NJktiNLl1akzt3NgoXqUpAQAAADg72ZMqUji1bdnP7zj1CQkIoWCAP48YOIl/enDRrHvYAJuxe+4OnT5/Tt99wqlQpx8oVf5IxUxHje+3nHjRq3FH3mkQLZhd0JEuWjJ9++onQ0FBCQ0O5cuUKBw4cMPzduWP8w/nx48dR1FPz061vB/z9A2hQqbVhZ/H1yzez5chKev7SiW6tfvro+Ts37w1X1qFn2NC1DSu3GJUPGdeP4OBg6pZr/sFdzHPkyYadvR3dWvfldcBrDuw6zM6T60ieMqkhAOo3vBfLF67hxtVbJr9fiXzr9x7D55Uf84f3Ik2ysKX16pQrQkhoKOv3HMP7pS+ODnYs27qfsgVzMrL7u4lm5QrmpFKnwWzcd+Kzgo7kbvGp8q+g432BgUFM/nsduTOlYdagLoYnrTnSp6TrqJms3HGQRpVKALD3xAWaVS1NqxplgbDgac/x84ag49TlG+w9eZ61kwd8ycci31HePDloUL8GP/40lN9+mwnAXwtXcOb0TkaN6k+x4tU/eO66dVvDlfXr1x2AxYuNN5gtXaY2d++GPVF99jTizHDatCnJkD4NqVLn5d69h/y1cDke7hcoUCA327eHfX8OH9aX/QeOsmNH9HsK+V+XJ08O6tevwU99hxnutYULV3D69E5GjfyF4iVqRHiejY0Nw4b+xL59R6hYsaGh/PCRE6xZPZ/WrRsxffpcAJ49e07RYtWMzv/jj4W88Pahc6eW9PlxCI8eeZE2bUrSp09D6jT5uHfvIQsXrsD94Xmje23Y0L4c0L0m0YjZTSRPnjw5yZIlo3HjxsyYMYPXr1/TunVrFixYwI0bN7h79y6LFy+mc+fOZM+ePaq7a1byFMjB4X3HjIIAL88nHD90ihJli+Do5GBym1VqlefenQecPn7OUJYyTXKKlynMnGl/8fzZC2ztbLG2tgp3rr2DHa8DAngdELZZzfNn3oZygNIVi5MpS3qmjJllcr/k+3jl6w9AnFjGy+zFixUTS0sLrK2tCAoKxv91IHFiGteJHdMFS0sL7E1YKc4/4DUBH5hrde2eOz6v/ChfOJfR0I7iebLiaG/HlgPvhh0EvA4khvO7+z2msyP+bzZNCgkJYfSfK2hZvSwJ47h+dt8katSqXZmgoCBmz15kKAsICGDuvCUULJiHJEkiHhL6IQ0a1ODmzTscPnLCqPxtwPExDg5hQ3mfvfmO9fPzx8/PH0fHsHstZ44sNGxYkz59hpjUJzEPtWtFfK/Nm7v4zb3mFuF5mTOnx9U1FstXrDMq37RpJz4+L6lXt1qE573vzp2wfQ5ixYoBgIP9B+41h7B7Lcfbe+3HoSa+S/mmoslEcnNhdkEHwIMHD1i6dCldu3Yld+7cuLq6UqFCBYYPH27Yxn3SpEmcPHmSp0+fRnV3zYatrS0B/gHhyv38/LG1syWticNIMmZJR5r0qcJlOQoVC3sa/cTrKXNXTOfcvYOcuXuAWYsnkTjpuy/ly+f/wTmGMy07NiZRkoR0/bEd3i98uHXjDja2Nvw0pAdTxs7C+4XPF7xb+R7yZEkLwKDpi7hy6z4ej5+x5eBJlm3bT6NKJXC0t8PezpasaVOwds8RNu47jrvXU67efsCAqQuJ4eRInbKFP+taa/ccIX/jH8jbsCc1ug9n437joQyBgWHBSERBjJ2tDVdu3TMM28ucJhkrth/k6p0HnP3nJpsPnCRLmuQArNp5mOfeL2lRvfQXfy7y/eTInoWr124ahpS8dfz4GQCyZ88cwVkfaCtHZjJlTMeSpWu+qC9Xr97k+fMXDBzwA8mSJaZXrw7EiOHM6dPnAfjtt2FMnz6PGzduf1H7ErWy58jMtYjutRNnwo5/4F6zezO3zc/PP9wxPz9/cuTIEm4OjI2NDXHiuJIkiRvVq1WgZ4/23L59j+vXbwNw9VrYvTZgQC/je+3Mm3ttwlCm/657TaIXsxteFZFXr16xfft2tm/fTsKECSlZsiSdO3emYMGCxIgRI6q7ZzZu3bhD9txZsbS0NPz4srGxJluuLAAkcItnUntV61QEYP2/go7kqZIBMHTcz5w/c4kebfqRKElCOvduw58rplG9REP8/QJ4cM+dCcOn8cOALvw0pAd+vv707zkMf78A2ndvgb+vP0vmrfzaty2RqEjOTHRpWIXZK7ey5/h5Q3nb2uXp2qiq4fWo7s3pM+FP+k2abyhLkiAu80f0IknCT8+7ypE+FeUK5SRxgjh4PX3Bki376DdxPi9f+VO/QlEAkrnFx8LCgtNXblKj1Lsls289eMQz77AfCd6vfInl4kzH+pXoOGw6dXqNAiBXxtQ0qVwSn1d+TPl7Pf3a1MX+zQ8FMW8J3eLj4e4ZrtzD4xEAidwSfHZbDRvUAmDx4lVf1BdfXz+6dO3HrJnj6dmzPUFBQfT7eSR37z6gQYMapE6dgqrVmn5R2xL13BLGx90jgnvtzf3n9oF77fr1W4SEhFCoYF4WLFhmKE+XLhXx44d9/7m6xuTp0+eGYzVrVGThwumG1ydOnKFdu96GuRm+vn507fozM2eOo2ePsHvt51/e3Gv1w+61atU1FzKqRZcdyc2F2QcdmTNnpkiRIhQuXJjChQuTPHlyAgICOH36NOPHj+fgwYNR3UWz8ffcFQwZ248REwcwe+oCLC0t6dCzFfEShH3p2ZmwypeFhQWVapTl4rkr3Lx22+jY22FaXp5PaN+oB6GhYQugeTx8xIRZI6lSqwIrFq0F4M/pC1m7fBNJkiXi1vU7eL/wIX6CuLTr3oLOzftgZW3Fz4N7UapCcR57PmHUwAmcPnYOMR+J4sUmV6Y0lCmQg1guTuw/eZHZq7YRN1YMGlYqDoCTgx2pk7qRPV1K8mdLz+Nn3vy5ehs9Rs9i3vCeRitLRWTByF5Gr2uWKkj9H0cz+e91VC+ZH3s7W1xjOFO+UE7W7zlKqiQJKZ0vO4+ePufXOcuNhnkBJIzjyrJxfblxzx0baytSJk6ApaUlY+euJEXiBFQonJtTl28wbv4qvJ6+oHT+7PzQrCY2Nmb/lfif42Bvb5iE+z5//3cTcz+HhYUF9epV5/Tp81y5cv2L+7N06Vq2bt1D+nSpuXX7Lp6ej3FwsGfkiF8YOHA0L1++on//njRtUpdXr14xZOh41q7d8umGJcrZO9gT8GY48Pv8Az5+rz158owVK9bTtGkdrvxzjbVrt5AoUUIm/jaM169fY2tri4ODA/DccM6evYeoULEBsWLGpGSpwmTLmincQi5Ll61l67Y9pEuXmtvv3WsjRvzMwEFjwu61X3rSpGkdXr30ZejQ8axdp3tNzJfZ/Re2WLFiFC5cmCJFilCgQAFixYrFo0ePOHToENOmTePQoUOcPHnSMNRC3lk6fxVuiRLQqnNTajaoAsD505eYM3UBHXu1xveV72e3la9QLhImSsD8mYvDHXs7hGvLuh2GgCPs9U5GTwsiZ95shqADwoZhPfF6Nwzuh4FdObz/OEf2H6d73w4UKJqXXu1+Jn/h3MxcNJHSuavh422c3paosfnACYbOWMy6qQMN8x/KFMhBSGgIvy1cS8WiuXF2dKDtkCnkzZyWfm3qGc4tkC09NXuOYN7aHfRsWsOk69rYWNOwYnGGzVzCpZv3yJUxbGjggPYN8X8dthzu+PlhE4GrFMtLkgRx2Xn0LI727/bjsbG2IkPKJIbXt+57sHTrfhaM/IEXPq/oMvJ3WtUsS94s6Rg4dSGzVm6lc4PKX/pRSSTx8/cPt88SgP2bf9cRDWmJSLFiBUmSxI1Jk//46j49f/6Co8fezSH66aeueHk9Zt78pbRs0YB2bZvSrHlXUiRPwt+Lfidb9pIaBhMN+Pv5G4ZKvc/e7tP3WqfOfXFwsGfM6IGMGT0QgEWLVnLz5h1q1qzEy5evjOp7ej5m164DAKxavZGffuzC5k1/kylzUR498jLUe/78Bcfev9d+7IKX12Pmz19KixYNaNu2Cc1bdCV58qQsWjSd7DlK6V77jpTpMI3ZBR27d+8mMDCQ5cuX07VrVw4fPsytW1rZ6HNNHPU7f05fSJoMqXjp/ZKrl2/Q8+dOANy+cfez26lSuwLBwcFsXBV+9RdPj7AvxCdeT4zKQ0JCeP7sBTH+Nen4fdlzZ6F8lVJULd4AgMq1yvP7+DmcOXGeMyfOU69ZLUqUK8r6FZs/u68SeZZu2U+GlEnDTbgukTcba3cf5crN+1hYWnD9rjt9WtQyqpM8UXxSJU7ImSs3v+jab6/54r3/WLs4OTC5b3vcvZ7ywPMpieLFJlH82DT9eTyuMZyJ8a8nhe8bM28VlYvlJVOqpKzfc4wYzk60qVUegJY1yvCHgg6z5OHuSaLECcOVJ0wYNtTlofujz2qnUcOaBAcHs/QL53N8SPLkSejZoz2VKjUkNDSU+vVr8MfshezZE5aFb9q0LvXqVWfUqEnf9Lry7bl7eJI4UQT32psllN0/cq95e/tQu05rkiZNRPLkSbl79z537z5g7541eHo+5sUL749ee9XqjQwb1peqVcsZTWR/X/LkSejRoz2VKjd6c69VZ/bsRezZcwiApk3qUq9uNUb9Ovlz37LId2V2Qcf58+fJnDkzDRs2JGvWrBw6dIgDBw5w6NAhbt++HdXdixa8X/hw6uhZw+uCxfLh/uBRuGFSH2Jja0O5KqU4dvAUno/CL0l88dwVAOL/ay17GxtrXGPH5Nnj5x9s+5cRvVnwx1Lu3Q5bKSZ+grh4vvdUx+uRl8lzTyTyPHnhE+EP+aCgsHHHQSEheHuHZdCCQ8LvMxoUHExQ8Jc9Crr/5t6LHcHQLLd4sXGLFxsIm8dx6cY9yhT48Gp2e0+c5+w/N1k/dRAAXs9eEM/13XyweK4x8Xwa8dLPErXOnr1IiRKFcHFxNprgmy9fTsPxT7G1taVmzUrs3Xv4oz8cv8To0QNZv2GbYb8Qt0QJcH/47hoP3R9F+ENWzM/Zs5coUTyCey3v599r9+495N69hwDEjBmDXLmysnr1px+ivV2tKmbMD89THf3rADZs2GbYLySRWwKjoNvd3SPCAF3EXJjd6lU5cuTA1dWVihUrsmbNGtKmTcuMGTO4fv06Dx8+ZMWKFfTs2ZP8+fNjbW12MZPZqVi9LNlyZWbBrMVGQ6HcEicg5ZvVfP6teJnCxIwVg/UrI/6iPHrwJI+9nlC1dgVs30tF12xQFWtraw7uPRrhebUaVCVh4gTM/O1PQ9kTr6ekSpMCAGtrK5KlSMpjzycRni/fX3K3+Fy5dZ/bD41/qG0+cAJLSwvSJU9EikRhweeWA8a7Ml+6eY/bDx8ZDXHyC3jNrfsehonfAE8jWL3slZ8/CzfuxjWGM5neLFzwIZMWriM4JJimVUtFeDwwMIhx81bRtnYFw7K+cWK6cNfdi6A3kzZvPnhEnFhalMIcrVq1EWtra9q0aWwos7W1pXmz+hw9eor798N+4CVNmoj06SNeoa9ixVK4usb64gnkH1K8eCEqVihFv37DDWWej7xInz6N4XXGDGnxeBR+crKYnw/da82av73X3IGP32vvGz68L9bW1kZD+uJ8YJnulq0aAXDy5NkIjxcvXogKFUrR7+cRhrJHjx4b9SNDhrQ88vCK6HSJJKEhUfcXHZnlr/aXL18aVquCsJ05c+TIQeHChSlUqBA9e/Zk7NixBAQEcOLECYoXLx7FPTYPeQrkpFPvNhzcc4TnT1+QPXdWajWswr6dh1gwa4lR3dFTh5CvcG4yxM8brp2qtSsQ4B/Atg27IrxO4OtAxg6ZzOipQ1i4dhbrlm/CLUlCmrZtwPHDp9i+cXe4c5ycHOnxSycmjpjOq/fmlmzdsItOP7TBwtKSXPmyYWdvy94dWhzAXLSoXoaDpy/Rov/EsB3JXZzYd+ICB05folaZQsSPHYv4sWNRMHsG1u05yis/fwpmz4DXM28Wb96Lna0NTaqUNLR34dptWg+aTId6FelUP2wo05It+9h97BzF82QlYVxXHj/zZs2uw7g/fsbIbs2MJnfPWbWN6/fcyZo2OVaWVuw+dpZDZ6/QpWEVw5K4/7Zw4x4AmlQuYSgrkjszI2Yvo+9v88ieIRWzlm+mVplC3/4DlK927Phplq9Yz4jh/YgfPy43rt+madO6pEiRhHbtfzDUm/vnJIoXL4SNbeJwbTRsWAt/f39Wrd70wetUrlyWbNkyAWFZ26xZMxo2EtywYRvnz182qm9pacmE8UMYP+F3w5NtCPvhOmpUf7wePyF5siRkyZLBsMu0mLfjx0+zYsV6hg/rS/x4cbl+4zZNm9QhRfIktG/f21DvzzmTKF68ILZ27x6o9OndmcyZ03Ps+GmCgoKoVq085cqWYODA0UaBRKNGtWjXtinr1m3l5q27uLg4UbZsccqWKc6GDdsMQ6XeZ2lpyfhxg5kwYYbxvbZ6I6NG/sJjryckSx52rzVv3jWSPh2Rr2eWQce/hYSEcOrUKU6dOsXu3bspWrQojRs3pmDBghQu/Hl7APwXPPLwJCQ4mNadmuLk7Mj9uw+ZNGoG82YsMizD9ylOzk4UL1OYvTsO8tLn1QfrrV22icDXgbTt1oI+g7rh7f2SZQtW8duI6Yblet/X8YfWPHroyaol643Kp4yZSew4sejcuw2PPZ/QvXVfnj15btL7lsiTJ3MaFozsxe9LN7F0y36ev3xF4vhx6NqoKi1rlDHUm/RTO+av28mWAyc5ePoSNtbW5MyYmi4Nq5Ay8ceXNM2ZIRVn/7nFqh2HeP7yFQ52tmRNk5whnRuTP2t6o7ppkydi57Gz7Dl+nuCQENIlT8S4H1pRrlCuCNt+8tybWSu2MKp7c6PgJU5MFyb0acPYuSs5fO4KJfJmpUO9Sl/xSUlkatmyO3cH96Fxo9q4usbk/PnLVK/RnAMHIs6qvs/FxZlKFUuxafNOvL0/vCdQrZqVaNbs3UIIOXNmJWfOrAA8uO8eLuho17Yprq6xGDt2mlH5zFl/kSJFMnp0b8erV760aduLS5ci3uFczE/LVj0YPLgPjRrVenOvXaFGzRafvNcuXLxM9eoVqFKlLFZWVpw/f5mGDduzctVGo3oHDx6nYIE81KtXnQQJ4hIUFMzVqzfo3Wcw06bNjbDttm2bhN1r44zvtVmz/iJFiqR0f3OvtW3bi0uXda99V6EWn64jBhZA+IHYZsLW1pb8+fNTpEgRw2pWMWPGBMJ2CT1+/DgHDhygf//+JredPl6eb91dkQid3TUyqrsg/xHOuVpEdRfkP+Lfm92JRJbA1w+iugsf5BGFI20S7t0bZdf+UmaX6ahWrZohyMiZMyc2NjZYWFjw5MkT9u/fz4EDBzhw4AAnTpwgKCgoqrsrIiIiIiKfYHZBx+rVYWvv37p1i6VLlxqCjCtXrkRxz0REREREwkTXCd1RxeyCjvr163PgwAE8PDyiuisiIiIiIvINmN2SuStWrPhgwBEnThxu3LhBgQIFvnOvRERERETeCQ2xiLK/6Mjsgo6PsbKyIkWKFDg4OER1V0RERERE5DOZ3fAqERERERFzpzkdpolWmQ4REREREYl+zDLTYWdnR/Xq1UmZMiVPnjxhw4YNeHh48PLlS4YMGcLNmzejuosiIiIiIvKZzC7oiBcvHocOHSJlypSGzYd8fX2pUaMGO3fuZOjQoVHcQxERERH5rwvVjuQmMbvhVQMGDCBFihT89ttvVKlShR49euDn58fMmTOjumsiIiIiIvIFzC7TUa5cORYsWECfPn0MZY8ePeLvv/8mXbp0XL16NQp7JyIiIiKiieSmMrtMR7JkyThw4IBR2YEDB7CwsCBBggRR1CsREREREflSZhd02NnZ4e/vb1T29rW1tdklZkRERERE5BPM8ld8ihQpyJkzp+F1zJgxAUibNi3Pnz8PV//06dPfq2siIiIiItF2Z/CoYgGERnUn3hccHExoaPguWVhYhCt/W/YlGZD08fJ8cR9FTHF218io7oL8RzjnahHVXZD/iLerS4pEtsDXD6K6Cx90N0+pKLt2shO7ouzaX8rsMh0tW7aM6i6IiIiIiHxUBM/I5SPMLuhYsGBBVHdBRERERES+IbMLOkREREREzJ3mdJjG7FavEhERERGR/y8KOkREREREJFJpeJWIiIiIiIk0vMo0ynSIiIiIiEikUqZDRERERMREWjLXNMp0iIiIiIhIpFLQISIiIiIikUrDq0RERERETKSJ5KZRpkNERERERCKVMh0iIiIiIiYKDVWmwxTKdIiIiIiISKRSpkNERERExEShIVHdg+hFmQ4REREREYlUCjpERERERCRSaXiViIiIiIiJQjSR3CTKdIiIiIiISKRSpkNERERExERaMtc0ynSIiIiIiEikUtAhIiIiIiKRSsOrRERERERMFBqi4VWmUKZDREREREQilUmZjp07d37RRUJDQylTpswXnSsiIiIiYm5CQ6O6B9GLSUFHiRIlvugiofq3IiIiIiLyn2VS0GFlZRVZ/RARERERiTY0p8M0mtMhIiIiIiKR6psGHa6uriRJkuRbNikiIiIiItHcVwcdMWLEYOLEiXh4eODl5cWtW7cMx/Lly8fGjRvJlSvX115GRERERMRshIRaRNlfdPRVQYerqytHjx6la9eu3Lt3j8uXL2Nh8e6DOHfuHIULF6Zx48Zf3VEREREREYmeviroGDx4MOnSpaNBgwbkzZuX5cuXGx339/dn7969lCpV6qs6KSIiIiJiTkJDLaLsLzr6qqCjWrVqbNiwIVyw8b7bt29rnoeIiIiIyH/YVwUdbm5uXLp06aN1AgICcHJy+prLiIiIiIhINGbSPh3/9uTJE5ImTfrROhkyZMDd3f1rLiMiIiIiYla097VpvirTsW/fPqpXr07ixIkjPJ4xY0YqVKjAjh07vuYyIiIiIiISjX1V0DFixAisrKw4ePAgjRo1Im7cuEBYdqNVq1bs2rWLgIAAxo4d+006KyIiIiJiDrRkrmm+anjVhQsXqF+/Pn/99RcLFiwAwMLCggsXLmBhYYGPjw/16tXj+vXr36SzIiIiIiIS/XxV0AGwfv16UqZMSfPmzcmfPz+xY8fG29ubo0ePMnfuXJ48efIt+ikiIiIiItHUVwcdAM+ePWPixInfoikREREREbMXXffLiCpfNafj36ysrIgVKxZWVlbfslkREREREYnGvjrosLS0pEePHpw5cwZ/f38eP36Mv78/Z86coXv37gpAREREROT/Tmho1P1FR181vMrJyYmtW7dSoEABQkJCuHv3Lo8ePSJBggRkypSJ8ePHU6dOHcqXL4+vr++36rOIiIiIiEQjX5XpGDp0KAULFmTx4sWkTp2a1KlTU6hQIcM/L1myhEKFCjF06NBv1V8RERERkSinJXNNYwF8cZLm3r17PHz4kPz583+wzrFjx3Bzc/vkzuXfW/p4eaK6C/IfcXbXyKjugvxHOOdqEdVdkP8IC4vo+aNHop/A1w+iugsfdDxx9Si7dt4Ha6Ps2l/qqzIdceLE+eRu4zt27CB27NhfcxkREREREYnGvmpOx7Vr14gfP/5H68SLF88sNwd87P8iqrsg/xF6+izfi+/D/VHdBfmPcEhUNKq7IBLltGSuab4q0zFp0iTq169PpkyZIjyeJUsWGjRooD08RERERET+w0zKdBQtavxk49q1a+zatYsTJ04wf/58Dhw4YFi9qmjRojRr1oytW7eaZaZDRERERORLRdcJ3VHFpInkwcHBhEawOPDbCWXvH3t/klloaCjW1t9k8/NvJo5L2qjugvxHvPB/FdVdkP8IDa+S70XDq+R7CQ58GNVd+KAjbjWj7NoF3FdH2bW/lEmRwNChQyMMOkRERERERD7EpKBjyJAhkdUPEREREZFoQ4/hTfNVE8lFREREREQ+xbwmWoiIiIiIRAOaSG6ar850JEmShBkzZnD9+nV8fX0JCgoK9xcYGPgt+ioiIiIiItHQV2U6UqZMydGjR3F1deXixYvY2dlx584d/P39SZUqFTY2Npw9e5bnz59/o+6KiIiIiEQ9bQ5omq/KdAwaNIiYMWNSunRpcuTIAcDcuXPJlCkTKVKkYN26dTg5OVGnTp1v0VcREREREYmGviroKFOmDJs2bWLfvn2Gsrf7c3h4eFC/fn0ARo4c+TWXERERERGRaOyrhlfFjRuXK1euGF4HBQXh6OhoeP369Wu2b99OjRo1vuYyIiIiIiJmJSSqOxDNfFWm4/Hjxzg5ORm9TpEihVGdoKAgYsWK9TWXERERERGRaOyrgo5r166ROnVqw+tjx45Rvnx5UqZMCYRlQurUqcONGze+rpciIiIiImYkFIso+4uOviro2Lx5MyVLliRmzJgATJw4ERcXF86dO8exY8e4evUqbm5uTJky5Zt0VkREREREop+vCjp+//13SpQoQXBwMAB79+6lQYMG3LlzhyxZsvDo0SO6dOnC7Nmzv0lnRUREREQk+vmqoMPHx4djx47x8uVLQ9mKFSvIkiULjo6OZMyYERcXF3bs2PHVHRURERERMRchoVH3Fx199Y7kn5IhQwZKlCgR2ZcREREREREz9VVL5oqIiIiI/BeFRNMJ3VEl0jMdIiIiIiLy36ZMh4iIiIiIiaLr0rVRRZkOERERERGJVAo6REREREQkUpk8vGrjxo0m1c+aNauplxARERERMWshUd2BaMbkoKNChQomXyQ0NJouKCwiIiIiIl/N5KAjZcqUkdEPEREREZFoQxPJTWNy0HH37t3I6IeIiIiIiPyf0kRyERERERGJVNqnQ0RERETERJpIbhplOkREREREJFIp0yEiIiIiYiJlOkyjTIeIiIiIiEQqZTpEREREREykJXNNo0yHiIiIiIhEKgUdIiIiIiISqTS8SkRERETERCEaXWUSZTpERERERCRSKdMhIiIiImKiEE0kN4kyHSIiIiIiEqkUdIiIiIiISKTS8CoREREREROFRnUHohllOkRERERE/o/Z2try66+/8uDBA3x9fTly5AhlypQxuZ1t27YRGhrKlClTTD5XQYeIiIiIiIlCovDPVPPmzaNXr14sWrSI7t27ExwczKZNmyhcuPBnt1GzZk0KFiz4BVcPo6BDREREROT/VN68eWnYsCH9+vXjxx9/5I8//qBUqVLcuXOHMWPGfFYbdnZ2jB8/ntGjR39xPxR0iIiIiIiYKMTCIsr+TFGnTh2CgoKYNWuWoSwgIIA5c+ZQqFAh/sfeXYdHca0BHP7FhSQQCHEI7k7x4O7uEKy4FJeiRQrFSuHi7u5O0eLuLgkSTyBGPJn7x8LCsgmwQEgC33uf8zx3z5yZObNMJ/vNMWdn508eY+jQoejr6zN9+nSdv6e3JOgQQgghhBDiB1W0aFEePHhAaGioRv6FCxcAKFKkyEf3z5QpE8OHD2fYsGFERkZ+cT1k9iohhBBCCCFSEWNjY0xMTDTyoqKiiI6O1irr4OCAt7e3Vv7bPEdHx4+ea8aMGVy9epWNGzd+RY2lpUMIIYQQQgidKcmYRowYQUhIiEYaMWJEgvU0MzMjKipKK/9tq4WZmVmi11ipUiWaNm1K//79P+9L+Qhp6RBCCCGEECIVmTx5MjNnztTISyiwAIiIiNBqFQEwNTVVb0+IgYEBs2fPZvXq1Vy6dOkrayxBhxBCCCGEEDr7kqlrv5Xo6OgEu1IlxNvbGycnJ618BwcHALy8vBLcz83Njdy5c9O9e3dcXFw0tllaWuLi4oKfn1+iQcuHpHuVEEIIIYQQP6hr166RK1cuLC0tNfJLlSql3p6QzJkzY2xszJkzZ/Dw8FAngA4dOuDh4UGNGjU+ux7S0iGEEEIIIcQPasuWLQwZMoRu3boxY8YMQDUQvVOnTpw7d44XL14AqlmqzM3NuX//PgAbNmxIMCDZsWMHe/fuZfHixZw/f/6z6yFBhxBCCCGEEDqK1225jGRz4cIFNm3axOTJk7G1teXRo0d06NCBLFmy0KVLF3W5VatWUalSJfTerANy//59dQDyIXd3d3bu3KlTPSToEEIIIYQQ4gfm5ubGhAkTaN++PdbW1ty4cYN69epx8uTJ71YHPVQzb/10MljmTO4qiJ9EcOTr5K6C+EmEe32/Px7i52bmWD65qyB+EnExCQ9yTgnWOLRNtnO3816bbOf+UjKQXAghhBBCCJGkpHuVEEIIIYQQOvopuwp9BWnpEEIIIYQQQiQpCTp+ILnz5GDpyn+4dP0Iz3yuc9/9PLv3r6Vmrcoa5YoWL8TUmWM5cmIb3oG3CQh5kOgxM2bMwOx5k7n7+CzPfW9w9L/tNGhUK8GyjZvW5eh/23nhd5N7T84x63+TSJ/eWqtcQMiDBFO/Ad2+7gsQySpfvlysX7+Q+/fOEBz0CG+vmxw9spW6datrlCvxSxHmzP6T8+f2E/7ag5hoz0SPaWtrw5LFM/F8cZ2Q4EdcOH+Apk3raZVr2LAWe/es5anHZcJCn+D+5BIbNiwif/7c3/w6RdK6c/8RfYaOo2yt5vxSpRGN2vVgzeaEZ0gJCQ2jQt1WFChXm0PHNMezPHrylIGjJlGreSd+qdII1zot6dBrCMdPndM6zpZd++nYewgV6rWmaKX61GzWkVGTZuLp7atVtkC52gmmJas3aZU9e/EqnfoMw7VOS8rUbEarX39j14EjX/jNiOSQJo05Y8cMYu/uNfj53CI22hO39i00yujp6eHWvgXbty3H/fFFgl895NrVI/w+4rcEV4Hu3s2NDesX8uTRBWKjPVm65O8Ez13etZT6mGEhj3nx7Cp7d6+hbJlfkuRahUhq0r3qB5IpsxMWlmnYuH47Pt5+mJmbUr9BTdZuWsjAfqNZtWIjANVrVKSdW3Pu3LrPU4/n5MiZLcHjWVimYe+h9WTMaMPCBSvx8w2gUePaLFs1m+5dBrJ18x512U5dWjPt7z84cewMo3+fjKOjPd16ulGkaEFqVmlGVJTmqpnHjp5i0/odGnk3rt/5tl+I+K4yZ3bG0iINq9dsxtvLF3NzMxo3rsOO7Svo2XMoS5aqBr3Vql2Fzp1bc/PmXZ64PyN3ruwJHs/S0oLjx3ZgZ2fDnP8txdfHj2bN6rNh/ULau/Vmw4Yd6rIFCuThVVAwc/63lMCAl9jZ29KxQ0vOnN5L+QoNuHFD7q3U4PT5y/QZNo68ObPTo2MbzM1Nee7pja9fQILl/7dkNRFRUQlu8/Lx5XV4BA1qV8PWJj0RkVEcPn6aPsP+YOzQvjRvWEdd9u6Dxzg52FPJtTRWlhZ4evmyZfcBTpw5z9YV87DNmEHj2GVKFKVB7WoaeXlzat7Hx06eo9+I8RQukJdenduip6fHwaP/8fuE6QQFheDWqvGXfEXiO7OxSc/oUQN5+vQFN27cpVKlslplzM3NWLb0b86du8yixavx8wugdOnijB0ziCqVXalWo7lG+SGDe2FpacHFi1dxcLBN9Nw5c2YjPj6eRYtX4+Pjh7V1Wtq0acqxo9to0NCNg4eOf+vLFTpKLVPmphQye9UPTl9fnyP/bcfU1IQyv6haKDJmzEBoaBiRkVFMmT6GX7u1w8Yql9a+ffp1YdzEYTSu58bJ/1RvB1V/ODfj5GRPkfyViYmJwcjIiLuPznDn9n0a1Gmn3r9GrUqs27SI4UMmsGThanV+QMgDlixaw/DB45P46lOGn3n2Kn19fS6cP4CJqQkFC1YEVK0XISFhREZG8s+sifTq1QkjYyetfQcO7MFfU0ZTvUYLjh8/Dajuv9OnduPs7Ej2HKWIiYlJ9Ny2tjZ4uF9i+fIN9O4zPGkuMIVJzbNXhb1+Td1WXSlSIC9/TxqJvv7HG+IfPvGgecc+9OjUhv8tWc3Mib9To/LHZ1SKi4ujRed+REdHs3v94o+WvX3vIS279KN/j078+t6b7QLlatO6SX1GDur10f279v+dx+7POLB5GcbGxgDExsZRv01XzMxM2bZy3kf3T+l+ltmrjI2NsbZOi6+vP8WLFeL8uf107jKAVe+1bBkZGfFL8cKcPXdJY99RI/szbuwQatZqxZGj7/7bzJzZiWfPVC28QS8fsHXbXrr8OuCz6mNmZsrD+2e5fv02deu3+/QOP4CUPHvVSsfkm72qg5fMXpWknJ2dKVGiBNbW2l12RMLi4+Px8vTGKq2lOs/fP5DIyITfDr6vdNlf8PcPVAccAIqisHPbPuzsbSnrWgKAvPlyks46Ldu37dPY/9CB44SFhtG4aR0SYmpqgomJ8Zdclkgl4uPjef7Ci3RprdR5fn4BREZGfnJfV9dS+PkFqAMOUN1/m7fsxsHBjgoVSn90fz+/AMLDI0ibzuqj5UTKsPfQcQJfvqJf9w7o6+sTHhFJfHx8ouWnzFpA1YplKVa4wGefw8DAAHs7G0LCPv0iwMnBDoDQsLAEt0dGRWm14L7v9etwrCwt1AEHgKGhAdZprTA1ludeahEdHY2vr/9Hy8TExGgFHAA7dh4AIE+eHBr5bwOOLxEREYl/QCDp0qX94mOIbyc+GVNqlKKCjpIlSzJ69GgyZNBsynZwcOD48eN4eHhw9uxZfH19mTZtWjLVMuUzNzcjfXprsmTNRI/eHalavQInT5zV+TjGJsZERmj/OAx/k1e4SAF1OSDBshGRURQslE+9uuVbrdo05pnPdTz9b3H6wj6aNtfupy9SJ3NzMzJksCZbNhd+69eVWjUrc+zYKZ2PY2JsTERC91S4Kq9YsUJa29KmtcLGJj0FCuRh0cLppE1rxbGjup9bfH/nLl3FIo05fv6B1Gv1KyWrNaZUjaaMnzZH68f9waMnuXbzLgN7dUnkaO+ER0TyKiiYZy+8WLVhO6fOXaJ08cIJlg0KDiHwVRC37j5g1KSZAJQqXkSr3I79/1KiamOKV2lIg7bd2HvomFaZEsUK8cj9KXMWreLZCy+evfBiwfJ13L7/kE5tm2uVFz8ee7uMAAQEvvyq41haWpAhgzW5c2dn4oThFCyQl6Nf8EwVIrmlqDEdvXr1olSpUkyYMEEjf9WqVZQvX54TJ05w6dIlqlWrxoABA7h9+zYrVqxInsqmYOMnDadjl9aAqjvBnl2HGPYFXZkePXSnYqWyOGdy5MXzd82bZcqqBrE5OKreBD559JT4+HhKlS7O+rXb1OVy5MhKxjd9odNZp+XVyyAAzp+7zM7t+3nm8QJ7B1s6d23LwqUzsbKyZPnS9V90zSLlmDZ1LN26tQdU99/2Hfvp99sonY/z4MFjqlYtr9EVAaCca0kAnBzttfY5dWo3eXKr3iqGhoYx6c9ZLFsu91Rq8PSFF3FxcfQb/geN69Xktx6duHj1Buu27CI07DXT/lB1kYuMimL6/5bQvmVjnBzsEhzs/b5pcxazeaeqFVZfX59qFcvy+8CEu0ZVadSO6GhVl710aa0Y0b8HZUsW0yhTpGA+alYpj7ODPX4BgWzYtodhf0wl9PVrWjV+9/Kke8c2vPDyZdGqDSxcqboHzUxN+HvSKKqUL/NlX5JIVQYP6kVwcAgHDmgHpbrYsG4BNWuqJoSJiopi4aLVTJw06xvUUIjvK0UFHaVLl2bfPs0uOrly5aJKlSrs27eP+vXrA2BoaMiFCxfo0qWLBB0JWDBvJbt2HsTewZZGjWtjYGCAsZGRzsdZs3IzHTu3YunKfxg1/E/8/QNo2LgOdeqpZiMyMzUF4OXLV+zcvp+WbRrx4P5j9u75FwcHOyZPG010dDTGxsaYmr6bwaNujdYa51m7eitH/tvGyDEDWb9222d1/RIp1+w5i9m6bS+OjnY0a1ofAwN9jI11v/+WLV9Pt27tWb9uAYMH/4Gvnz/NmtWnUUPV2CQzM1Otfbr+OhBLKwuyZXWhQ4cWmJmaYmBgQGxs7Fdfl0ha4eERRERG0aJRHX4f0BOA6pXKERMTy+ad++jza3tcMjmxZPUmYmNj6ebW8rOO275lI2pUdsUvIJCDR08SFxdPTCL3w4LpE4iKjubJ0+fsOXiUiASeRWsWzND43KReDVp07sfshStpVKc6pm9mKzI2MiJLJidqVHalasWyxMfFs2XXfob/MY3FsyZRuEBeXb4ekcoMH9aXatUq0LvPCIKDQ77qWL+Pmszfsxbi7OyIW/vmGBsbYWhoQCJzKIjv6KccFP0VUlT3KgcHB+7fv6+RV7duXRRFYcGCBeq82NhY1q9fT4ECn9+X92fy6OET/jt+hk3rd9CmRXfSWJizdtNCnY9z5/Z9uncZRJasmdl/eCOXrh+hW4/2jBo+CVAN/Hxr4G+jOXzoBOP/HM7lG0fYc3Add+884OB+1Rue16/DEz1PTEwMSxetIZ11WnWXLZF63b//mKNHT7JmzRYaNe6ARZo07Ni+Qufj3Lx5l/ZufciWLQv//beT+/fO0Kd3ZwYNGgdAWJj2PXXu/GX+/fcECxetok7dtrRp04RJE0d85RWJ7+Htj/U61Stp5Netofp87dZdPL19WbFuK/26d8Dc3OyzjpvNJRNlShSlYe1qzJv2B+EREfQZOg5F0f65ULJ4YcqXKUGHVk2YMeF35i9by7otuz56fCMjI1o3rU9IaBh37j1U50+aOY/jp88z7Y/h1KlWiXo1q7D4n8lktEnPlH90fx6L1KN58waM/2MoS5etY+GiVV99vOvXb3P4yElWrNxIzdqtKVGiCMuWzvr6igrxnaWooCMmJgZDQ83Gl3LlygFw+vRpjXw/Pz9MTbXfdAptu3ccpFjxQuTIkVX3fXcepEAuV6pXakrNqs0pkr8yHh7PAXj8yENdLjQkjPate1E4X0Xq12pDkfyV6NVtCHb2GfH3DyQkOPSj5/H09AHA2loGx/1otm3bS4kSRcmVyNS4n9o3s0sxypSpg6trfbLnKMUT96cAPHz45KP7BgUFc+z4aVq3lqlJU4OMNukByPDBRCHprdMBqjU5/rdkNbYZM1CiaCE8vX3x9PYl4KWqv/zLoGA8vX0/OvgcoEZlV27dfYDHJwbzZnZ2JE+u7AmO1/iQva0NAMEhqkHnMTExbN9zkAplS2jMwmVkaIhr6V+4fe/hR2deE6lXtarlWbFsFvv2H6FX728/a15MTAx79vxL40a15TdQChCvl3wpNUpR3asePnxIlSpVmDt3LgCmpqZUqlSJK1euEBQUpFHW3t4eX9+P9+UVKqZmqjeIlu/NYKWLmJgYrl65qf5c8c085f8dP6NV1vOFN54vvAGwSmtJ4SIF2LPr4CfP4ZIlE/D1A+5EyvO2G1Raqy+//y5dvq7+XLWqaqrO96eg/Ni50743c5ZIufLlzsnZi1fxCwggq4uzOt8/IBCA9OnS4u3rx7MXXtRq3klr/4nTVX83zhzYjJWlRaLniXwzKP39ltrEREVFEf0ZwcELL82XJkHBocTGxSUYAMXGxhIfH09cfDy6dzoUKVnJEkXZsnkply/foFXrHsTFxSXJecxMTdHX18fSMs1nzQQoREqRooKOefPmsWLFCubNm8eZM2do3rw56dKlY9myZVplq1atyu3bt5OhlimXjU16AgI0f7QbGhrSslUjwsMjeHDv0VefI1t2Fzp2bs3B/Uc1WjoSMnrsIAwNDVgwd4U6L0MGawIDX2mUs7BIQ/deHQgIeMn1q/JvmlplzJgBf/9AjTxDQ0PatWtGeHgEd+4++Opz5MiRlW5d27Nn778aLR0JndvFxZkqlV25/F7AIlKuWlXLs3TNJrbtPqgxY9TW3QcxNDCgRNFCONhl5NUH/eMfPXnKnMWr6Ny2GYUL5FUHuYGvgsjwppXkrZjYWHYfOIKpiQnZs2QGVGtnvA4P1wqKb965z8MnHtSpXlmd9/JVkLrl5a3Xr8NZvWkH1umsyP9mEoP01mmxsrTgyIkz9Pm1PUZvxtSFh0dw/PR5srpkUncnEz+GPHlysGvnKjyePqdBow7fJBhI6LmWNq0VjRvX4dkzT61tQqR0KSroWL16NSVLlqRnz550794dUM1c9f54DoA8efJQpUoVfvvtt+SoZoo1458JWFpacPbMRby9fLG1s6FZiwbkyp2d0SMmq8dVOGdypEWrhgAUKaoaQzFwiGrg5vPnXmzesFN9zNMX9rFrxwFePPcicxZnOnVpzatXQQzqP1bj3P0GdCNvvpxcvnSDuNhYaterRpWq5Zk0fqZGK0mXbu2oXbcaBw8cxfO5N3b2GWnTrinOmRzp1W2IdDlIxebN+wsrS0tOnjqHl6cPdva2tG7dmLx5cjJkyB/q+y9zZifatm0GQPE3U5eOGKH6b/nZsxesXbtVfczr14+xdesenj/zJEvWzHTv5sbLl0H0/qDbwtUrRzh67BTXr98m6FUwOXJmpVPH1hgZGTJy5OTvcfniK+XNlYPG9Wqwfc8hYuPiKVG0IBev3uDg0ZP82r4lthkzaK0MDmBpoWrVKJA3F1UrvFst+o+ps3n9OpziRQpia5OBwJev2HPoGO5PnzOkb1f1mJDwiAiqNXGjVtUK5MjqgpmpKQ8fe7Bj3yEs0qShR8d3E1+s37aHo/+dpVK5UjjYZ8Q/4CXb9x7C29efyaMHq4MLAwMDOrRuwpxFq2jTbQANalUlLj6ebXsO4usXwJQxQ5LyqxTfWK+eHUmXLi0Ob9ZuqVevGs7ODgD8b+4y4uPj2bdnHdbWaZkxcz516lTV2P/J46ecO39Z/ble3eoUKpQPACMjQwoWzMvvb56Bu/cc4ubNuwDs2b0GT09vLly4ip9fAJkzO9HBrSWOjna0btszya9bfFpqXS8juaTIFckzZsxI1qxZefr0aYJdqGxtbXF2dub+/fu8/owm8oT8iCuSN25al7btm5E3fy7Sp09HWNhrrl+9zZKFqzmw/6i6XDnXkuzctybBY5w+eZ6GddurPy9aNpOSpYqR0daGl4GvOLDvCH/9OVurRaV6zUoMHtabXLmyo2+gz53b95n/v+Xs2nFAo1zFymXp89uv5MuXC+v06Qh/HcGVKzeY8/dijUUIfyQ/y4rkLVo0oFPH1hQokIcMGawJDQ3jypWbzJ23jD17/lWXq1ChDEcOb0nwGCdOnKFa9XdrGKxePZeyZUpgZ2dDQMBL9uz5lz/GT9d6wzd69EDq1K5KtmwuWFpa4OcXwMlT5/nrrzncunUvaS44BUrNK5KDqiVi8aqN7Nh7CL+Alzja29K6ST3at0x8XM6FKzfo3HeY1ork+w4fZ9vuQzx84kFwcAjm5mbky52Tts0aULn8u4UlY2JimDF3KReu3sDL25fIqGhsbdJT+peidO/YWr1IIMCZC1dYvm4LD594EBQcirmZKQXy5qJLu+YJruex99Ax1mzeydPnnkRHx5ArexY6tWlG9cqu3+YLS0Y/y4rkAI8enCPLmy7AH8qesxQAjx+eT3T/las2aaw4vnTJ33Rwa5Fg2fdXO+/ZowMtWzQkd+4cpEtnxatXwZy/cIUZM+Zz6vSFL72cVCclr0i+yCn5ViTv5pn6ViRPkUHH9/AjBh0iZfpZgg6R/FJ70CFSj58p6BDJKyUHHQuSMejokQqDjhQ1e9XnMjIyomvXrlrT6wohhBBCCCFSnhQ1pgNUAUWDBg3Inj07r169Ys+ePXh7q2ZDMjMzo0+fPvTv3x97e3seP36czLUVQgghhBA/IyWVTl2bXFJU0OHg4MDx48fJnj07enqqf8mIiAgaNGhAdHQ069atw8nJiQsXLtC3b1+2bduWzDUWQgghhBBCfEqKCjomTZpE1qxZmTp1KidPniRr1qyMGTOGRYsWYWNjw+3bt2nXrh3//fefTsc1NjbG5IPpCY2NjYiOlpmShBBCCCGESGopKuioXr06y5cv5/fff1fn+fj4sHnzZvbu3UvDhg1RFN3HvY8YMYJx48Zp5E2dPIepk+d8bZWFEEIIIcRPSKbM1U2KCjrs7Ow4d05z2tS3n5ctW/ZFAQfA5MmTmTlzpkaehXHC098JIYQQQgghvq0UNXuVgYGB1iqebz8HBwd/8XGjo6MJDQ3VSD9D16oBg3sQEPKAk+f2fLLslZtHCQh5kGC6cPWQRtlOXVqzdOU/XLt9nICQB8yZPyXBY+bKnZ3dB9bh4XmFw8e38kvJIlplevbuxKnzezEwMPiiaxTJx9jYmD///J2nHpcJCX7E6VO7qVpV92k09+9bT0y0J//MmqiR79a+BTHRnomm1q3frd1QtswvXDh/gJeB9zn872Zy586udZ6/Z45n757UN8Xgz+T2vYd0HziKUtWbULJaE7r2/517Dz4+YUhIaBgV6raiQLnaHDr2+VMGB7x8xR9TZ1OlYTuKVW5AjaYdGD35b40yc5euoUC52lqpWOUGGuWio6P5c+Y8ytdtSdVG7Vi4Yr3W+Xz8/ClRrTFXbtz+7DqK78/Y2JjJf/7OM4/LhAY/4syp3VT7jOdarlzZmTFtHCdP7CQs5DGx0Z64uDgnWNbExIRhQ/tw4/oxQoIe8dT9EhvWLyRfvlwa5fLmzcnxo9t4FXifc2f3UbpUca1j9f+tG9evHZW/ockkPhlTapSiWjoAsmTJQtGiRdWf06ZNC0DOnDkJCgrSKn/16tXvVbVUxcHRjv6DehAW9nlrRIwc/icWFuYaec6ZHBk5ZiDHjp7WyO/bvysWlmm4cvkmdvYZEzyevr4+K9fO5dWrIMaNnkrNOlVZvX4+JYpUIyxUVScbm/QMHtabLh1/Iy4u7guuUiSnpUv/pmmTusyevYRHj9xxc2vB7l2rqV69OafPXPysYzRqVJvSpbX/kAKcPHWODh37auX/1q8rhQrl4+jRUwBYWVmydetyzp+/wpIla3Fza8HGjYspVqwa8fGqR3O+fLno0qUNpUrX/sKrFUntzv1HuPUcjL1dRnp2bkt8vMLG7Xvo2Gco6xf/Q9ZEfsD9b8lqIqKidDqXt68/7XsOAqBFozrYZsyAf8BLbt5JeBr20YP7qFcwBzDQ13xft3zdVnYdOEK3Dq14/TqC+cvXkcnJgTrVK6nLzJi7lMqupSlWKL9OdRXf17L3nmsPH7nT4c1zrdonnmulSxWnT5/O3Ln7gLv3HlG0SIFEy65eNYf69WqwZOk6Zl1dhKOjHT17dOTUf7soUqwqz555oq+vz+ZNS3j1MohhwydSv151tm1dRu685QgNDQMgY8YMjBrZn9ZtesjfUJEqpLigY8KECUyYMEErf968eRqf9fT0UBQFQ8MUdwkpwvhJw7l88ToGBvqkz2D9yfL79x7Wyhs4pCcAWzbu0shvUKcdL56rFuvx8Eo46MuWPQs5c2WjcL6KeL7wZuP6Hdx3P0+JkkU5dkT1Y3HU2EGcPXOR4x8ENSLlK/FLEVq1bMTQYeP5+++FAKxes4VrV48wefIoKlRs+MljmJiYMPWvMUybPo8/xg3R2u7u/gx392caeaampsyZ/SfHjp3G19cfgNKli2NmZkrLVt2Iiori4KFjPHp4nhw5svLgzVvyGdP/YOnSddy9+/BrL10kkTmLV2FiYszahTNJl9YKgPo1q1C31a/8s3AFs/4cpbXPwycebNq+lx6d2vC/Jas/+1zjp87G0MCADUv+UZ/rY2pUdsU6XdpEt584fZ4OrZrQuW1zQNWqcezUOXXQceX6LY6fPs/udYs+u47i+3v/uTbzvefa9atHmDJ5FOU/8lzbvecQGTLmJSzsNQMHdE806HB0tKdJ47rMmDGfYSPete6eOnWBw/9upnGjOvwzezE5c2YlT+4cZM1egufPvVi9ZjO+3rcoU7o4h/49AcDECcM5eeo8/x7WbXIdIZJLivrF3qlTp+Suwg+hTNlfqN+wJpVdGzFl2ugvPk7T5vXxcH/OxQuagcXbgONjzMxUs4UFB4UAEBERSWRkJGZmqreFhQrno2mL+lQoU++L6yeST5OmdYmNjWXJknfdlaKioli+YgOTJo7A2dmRFy8+fp8MHtwTfX19Zs5ckGDQkZB69apjZWXJ+g3b1XlmZqZERkYR9eZt98uXQQCYm5sC0KBBTYoUKUDrNj10uUTxnV25fgvX0r9oBAEZbdLzS9GCnDhznvDwCI3WBoApsxZQtWJZihVO/K3yh548fc7Jc5cYNbg36dJaERUVjb6BPkYfeYGlKAphr1+TxtxcPZ37+yKjo7GytFB/TmtlyXNP1fpS8fHxTPlnIZ3bNMPeNuGWYZEyNH3zXFv8Bc+1V6+CPusclpZpAPD189fI9/bxBVR/K0H1XFMdN1idHxERidmb/waKFilAm9aNKVKs2mdenUgKXzbS+OeVooKOVatWaeWZm5tjaWlJQECANB9+Bn19fSZPG8OalZu5e+fBFx+nYKG85M6TgxnT5n26cAIeP/IgOCiEoSP6smjBKho1ro2lpQU3rqv6M/85dTRLF63B/cmzTxxJpERFChfgwcMn6mb+ty5evAZA4cL5Pxp0ZMrkyNAhfejabaDWOK6Pad26MeHhEWzfvk+dd+3aLdKmtaR//+5s27aHfn27EhQUzP37jzE2NmbqX2MYP346QUFfPi5MJL3omBhMjI218s1MTYiJieXhEw8KF8irzj949CTXbt5l17pFeHr7fvZ5zl1UvUTJYG1Nl37DOX9Z1SJcpkQxRg/ug5ODndY+tZp3JjwiAjMzU6qUL8OQvl2xSf+uBblAnlxs2XWAEsUKER4Ryb7Dx2ndtD4A2/Yc5FVQCJ3aNvvsOork8annWpFPPNc+x+PHT3n+3IsB/bvz4METrl67haODHVMmj+TJk6ds3LQTgAcPnhAUFMzY0YOYM3cpzZvVx8rKgqtXbwIw6+8JzJu3gsePPb6qPkJ8Tykq6Hgrc+bMDBkyhPr16+PsrOrHqygKL168YNOmTcydO5dnz+THakI6dmlNpkyONG3Q4auO06yFaqDklo27v2j/8PAIhgwcy6z//Umvvp2JjY1l/JjpvHjuRdPm9ciaLTOtmv36VXUUycfewRYfbz+tfJ83b+scE/jh9r6pU8dy7dotNm3a9dFy77O2TkfNGpXYueugxlilp09f8PvIP5n85+9MmzqG8PAIunUfREREJMOG9SUiIoKFiz6/641IHlkyO3Pj9j3i4uLUg2JjYmK4cVs1zsLPP1BdNjIqiun/W0L7lo1xcrDTKeh4+uZH4x9TZ1Mgby6mjx+Bt68f85et49ffRrBt1TzMTFVvma0sLWjTtD6FC+TF2MiIy9dvsWHbHm7dfcDGpf9gkUb11rpXl7b0GDiaJm69ACheuADtmzciNOw1sxeuZMSAnph+sFaUSHkSe669bYVw+MRz7XPExsbSomVXVq/6Hzu2r1DnX7p8nfIVGxIcrOodEB4eQe++I1i8cAYDBnQnNjaWEb//ybNnnrRq1Yjs2bNQr0H7r66P+DrxsiK5TlJc0FGvXj3WrFmDpaUlHh4e7N69m9DQUCwtLSlUqBCDBw+ma9eutGvXjn37VG87J0yYwOjRX96N6EdhnT4dw0f2Y8bUeQQGvvri4+jp6dG4aV1uXLvNw0/MHPMx27bs5cjhk+TImZVnHi/w9w/EzMyUMX8M4c/xf/M6LJwhw/vQsnVjXr9+zV9/zmHfnn+/+Hzi+zEzNVV3Z3pfZKQq723XgIRUrFiWJo3rUK6cbl3rmjapi4mJCevXb9Pa9vffC1m7ditZs2Tm/oPHBAUF4+Bgx7ChfWjarAuGhoZM/Ws09evXxNfXj8GDx3Hm7CWdzi+SVqvG9Zgw/X+MmTyLzm2bEa8oLFyxHv/Al4CqC9NbS1ZvIjY2lm5uLXU+T3hEBAA26a2ZN+0P9N8MCreztWHo2L/Ye+g4zRrUAqB9i0Ya+1av7ErBfLkZ9sdUNmzby6/tWwBgb5uRzSv+x+MnTzEyMiSrSyb09fWZOnsRWTI7U7taRa5cv8W0/y3BLyCQqhXKMqTPrxgZGelcf5F0vua5potXQcFcv3GHrdv2cO78FXJkz8qwoX3YuH4hNWu3Vtdh48adHDx4nNy5suPu8Qw/vwDMzEyZPGkko8f8RVjYa0aPGkD7ds0Je/2aP8bPYOfOA9+kjkIkhRQ1ZW6ePHnYtGkTnp6eVKxYkezZs9O4cWPc3Nxo3Lgx2bNnp2LFinh5ebFx40Zy587NihUrGDFiRHJXPUX4fVR/gl4Fs3jh173VLedaEkcne7Zs+rJWjvcFB4Vw+eJ1/N+8pfxtUHcC/ANZt2Yrbds3o2PnVgzoO5KF81ayZMXfZM2W+avPKZJeRGQkJgm8uTU1VeW97Zf8IQMDA2b9PZ61a7dy6fJ1nc7ZunVjAgNfceDAsQS3+/kFcP7CFXU3qsl/juTosVMcO3aKkSP7U7mKK23b9mTnroPs3LmKtJ8xgFh8Py0b16WrW0v2/nuchu160Lh9T557etP5Tbck8zc/+Dy9fVmxbiv9unfQGuPxOUyNVfdozSrl1QEHQM3K5TE0MODarTsf3b9ujcrYZLDm3CXNsW5GhobkyZWd7Fld0NfX58nT52zYvofh/XsQHBJKryFjqVK+DDMn/M7Zi1dZuHKDznUXSetLn2u6sLKy5PjRbZw7d5mRo6awe/ch/p61kBYtu+LqWoqOHTQD6aCgYM5fuIKfXwAAw4f1xc8/gBUrN9KpYyu6dW1P9x5DmD17CevXzid79ixfXUfx+WTKXN2kqKDj999/JzAwEFdXV06dOpVgmVOnTlG+fHmCgoK4fPkybdu2laADyJbdBbdOLVm0YDX2DrZkyuxEpsxOmJiaYGRkSKbMTqSzTnz2lfc1a9GAuLg4tm359PoeusiU2YlefTrz+7BJKIpCk2b1WLl8Iyf/O8e6NVu5dOEajZvW/abnFEnDx9sPewdbrXx7e1X3A69Euru0b9eMXLmys3jxGlxcnNUJwNLSAhcX5wTfJmbK5Iiraym2bt1DbGzsJ+tXqmQxmjSpw9Ch4wFo2aIhM6bP59z5y/z11xyCg0OpW1cGYKY0v3XvyInd61g1bxrbVs1j49LZxMerhmpmyeQEqKbItc2YgRJFC+Hp7Yunty8BL1WtIS+DgvH09lVPlZyQjDbpAciQXnNWPwMDA9KmtSLkg/78CbG3zUhwSOhHy0ydvYh6NaqQL3cOTpy5QForS7q6taRwgbx0btuMvYcSDp5F8knsuebw5rnmrUM3vsQ0aVwHe3tbdu/RXP/qv5PnCA4OoWzZXxLd18XFmQH9uzNw4BgURaFVy0YsXrKGY8dPs2LlRs6du0zLFp+eOVCI5JKiuldVqVKFJUuW8OrVx7sGvXr1imXLljFq1Cjc3NxYu1YW/HJwsMPAwIAp00YnOGPV1VvHWDBvBaOG//nR4xgbG1GvQQ1On7yAj49239av8cfEYRzcf5Tz5y4D2v1nfXz8vkmfWZH0rl+/TaVKZbG0tNAYdFmyZFH19oRkyuyEsbEx//23U2tb+/bNad++OU2bdWbXroMa21q2bIS+vj7rEuhalZC//x7PnP8t5cmTpwA4Otrh5e2j3u7t7Yujo/1nHUt8X2mtLDVmozp36Sp2tjZkdckEgLevH89eeFGrufZshxOnzwXgzIHNGrNJvS9/npwA+PkHaOTHxMQQFBxM+o9MjQuq8YVe3r7kyaW9AOVbx0+f59rNO+zduBQA/4BAbDKkV2+3tcmgMUZFpAyfeq5dS+S5pgs7O9UMZgYG2u98DQwMProMwNS/xrB7zyH1eiEOjnZ4eb0LhLzkuSZSuBQVdGTIkAEPD4/PKuvu7k5cXJwEHG/cvfOQ9q17aeX/Pro/FhZp+H3YJDzerHng5OyAmZkZjx4+0SpfrUYl0lmnZYsOA3w/h2v5UlSrUZEyv9RS5/n7BZAzVzb155y5ssuYjlRi27a9DBrUk19/batep8PY2JgObi05f/6KeoaXTJkcMTc34/591digTZt2JhiQbN2yjH37jrB02VouXNBe+6V1q8Y8ffqC06cvfLJuHdxa4OzsyOTJs9V5vr4B5Mmdg8OH/8PQ0JDs2bOo1/kQKdf+wye4dfcBg/v8qu4K1a+rG6/eDLZ969GTp8xZvIrObZtRuEBedWtZRGQk3r7+WKe1Uq+zUaJoQdJbp2PPoWN0dWuFiYlqxqwd+/4lLi6eMiWKqY/78lUQ6a3TaZxr4/a9vAwKxrVUwm+kY2JimDZnMd06tCbDm30zWFvz/IUXsbFxGBoa8MTjGTafsX6S+L62vnmudf21rXqdjs95runiwZu/uy1bNGT8hJnq/Pr1a2BhkYZr124luF+limWpXasK+QtWUOf5+fqTJ3cO9ec8eXKyc+d+neskvlxq7eaUXFJU0BEQEEDWrFk/q2zWrFnx8/u2b+JTs5cvXyW4wF+PXqpZrN7fNm/hVMqVL4WNVS6t8s1a1CcyMordH7xpfl/NWpXJXzAPAEZGRuTPn1u9kOCBfUe5c1tzVV99fX0mThnJ3NlL8Xzhrc7ftfMgY8cPISDgJZkyOZIvfy56/DpIh6sWyeXCxats3rKbSRNHYGtrw+NHHrRv35wsWZzp1v3dv+HyZf9QsWJZjIxVXWPu33+c6B9qD49nWi0cAPnz56ZQoXz8NXXOJ+tlYZGGCROGM3r0FI0ZrrZt28vIkQPQ19enTNkSmJqasH//EV0vWyShS9dusmD5OsqWLEZaKytu3L7Hjn2HcC39C+2aN1KXS2hNDksLVatGgby5qFqhrDr/5p0HdO47jJ6d29K7SztA9SNyUO8ujJw4gw69h1C/ZhW8ff1Zs3knxQsXoFrFd/vXaNqRWlUrkDN7FkyMjbly4zb7D58gT85sNG+U8Or2q99Medr+vW4u5cuWYNLMuQz74y+KFMjLghXraVq/5pd/WSJJfPhce/TIA7cEnmsr3jzXDN8810A1VqNP784AlC2jCkh79+xEUHAIQUHBzJu/AoA9e/7l1u17jBo5gMyZnTl//go5smehV6+OeHn5sGz5eq166evrM2PGH8yYOZ/n762TtXXbXqZMHoV/QCCZMztTsEAe3Dr0SYqvRohvIkUFHcePH6dLly7MmjXro12srK2t6dKlC0ePHv2OtfvxWVimoXrNSvx78DihIYn3a67XsCat2zZRfy5UJD+FiuQHwMvTRyvo6NC5FdbWaZn9t+ZqvCuWrsfFxZmefToR/jqcvr1GcP/eo294RSIpder0G8/GDaFtm6ZYW6fl5s27NGzUgVOnzn/T87Ru3RiADRt2fLLsyJH98fT0ZuWqTRr5f4yfTsaMGRg1agA+vv60bNWNgICX37Se4uvY2mRAX1+f5eu28Do8AicHe/p27UCHVo0xNDT4pudqWLsaRkaGLF29mRnzlmJpYUGLhrX5rXtH9XS9oBo0fu3mHf49foqo6Bgc7W3p3LYZ3dxaqafVfV/Ay1csXLGOv8YO1ZiZKoN1Ov6eNIq/Zi/i7MWrVCpXip6d237TaxLfRsdOvzE+gefayU8816yt0zL+j6EaeQMHqhYk9fB4rg46YmJiqFS5CaN+70/tOlVp1bIhoaGv2bnrIKNGT0lw5sluXduT3jodU6fN1chfuGg1WbNkpv9v3Xj9OpwuXQdy5yvW5xK6k8UBdaNHCvrO8ubNy5UrV3j06BHdunXj7NmzWmXKlCnDwoULyZkzJ8WKFePu3btfdK4Mljm/trpCfJbgyNefLiTENxDudTK5qyB+EmaO5ZO7CuInERfzdQsyJqVpmZLv5cGQ56lveEGKaum4e/cubdq0YdWqVZw8eRIPDw+uX7+usU5H1qxZiYiIoE2bNl8ccAghhBBCCCG+nxQVdABs376dQoUKMXToUOrVq0ejRo3U27y8vFi0aBHTp0/nyRPtQdBCCCGEEEJ8D7IiuW5SXNABqpmpevbsSc+ePbG0tMTS0pLQ0FBCQz8+L7oQQgghhBAi5UmRQcf7JNgQQgghhBApjUyZq5sUtSK5EEIIIYQQ4seT4ls6hBBCCCGESGlSzPSvqYS0dAghhBBCCCGSlAQdQgghhBBCiCQl3auEEEIIIYTQUbx0sNKJtHQIIYQQQgghkpS0dAghhBBCCKEjmTJXN9LSIYQQQgghhEhSEnQIIYQQQgghkpR0rxJCCCGEEEJHMoxcN9LSIYQQQgghhEhS0tIhhBBCCCGEjmQguW6kpUMIIYQQQgiRpKSlQwghhBBCCB3F6yV3DVIXaekQQgghhBBCJCkJOoQQQgghhBBJSrpXCSGEEEIIoaN4mTRXJ9LSIYQQQgghhEhS0tIhhBBCCCGEjqSdQzfS0iGEEEIIIYRIUhJ0CCGEEEIIIZKUdK8SQgghhBBCR7IiuW6kpUMIIYQQQgiRpKSlQwghhBBCCB3JlLm6kZYOIYQQQgghRJKSlg4hhBBCCCF0JO0cupGWDiGEEEIIIUSSkqBDCCGEEEIIkaSke5UQQgghhBA6kilzdSMtHUIIIYQQQogkJS0dQgghhBBC6EimzNWNtHQIIYQQQgghkpQEHUIIIYQQQogkJd2rhBBCCCGE0JF0rtKNtHQIIYQQQgghkpS0dAghhBBCCKEjmTJXN9LSIYQQQgghhEhS0tIhhBBCCCGEjhQZ1aETaekQQgghhBBCJCkJOoQQQgghhBBJSrpXCSGEEEIIoSMZSK4baekQQgghhBBCJClp6RBCCCGEEEJH8TKQXCfS0iGEEEIIIYRIUhJ0CCGEEEIIIZKUdK8SQgghhBBCR9K5SjfS0iGEEEIIIYRIUtLSIYQQQgghhI5kILlupKVDCCGEEEIIkaQk6BBCCCGEEEIkKeleJYQQQgghhI5kRXLdSEuHEEIIIYQQIklJS4cQQgghhBA6UmQguU6kpUMIIYQQQgiRpKSlQwghhBBCCB3JmA7dSEuHEEIIIYQQIklJ0CGEEEIIIYRIUj9t9yonc5vkroL4SYREhSd3FcRPwsyxfHJXQfwkws7NT+4qiJ+EWfGGyV2FRMlAct1IS4cQQgghhBAiSf20LR1CCCGEEEJ8KRlIrhtp6RBCCCGEEEIkKQk6hBBCCCGEEElKulcJIYQQQgiho3hFBpLrQlo6hBBCCCGEEElKWjqEEEIIIYTQkbRz6EZaOoQQQgghhBBJSlo6hBBCCCGE0FG8tHXoRFo6hBBCCCGEEElKgg4hhBBCCCFEkpLuVUIIIYQQQuhIke5VOpGWDiGEEEIIIUSSkpYOIYQQQgghdBSf3BVIZaSlQwghhBBCCJGkJOgQQgghhBBCJCnpXiWEEEIIIYSOZJ0O3UhLhxBCCCGEED8wY2NjpkyZgqenJ+Hh4Zw7d45q1ap9cr/GjRuzYcMGHj9+zOvXr7l37x7Tp08nbdq0OtdBgg4hhBBCCCF0pCTj/3S1YsUKBg4cyNq1a/ntt9+Ii4tj3759lCtX7qP7LVq0iLx587JmzRr69evHgQMH6NOnD2fPnsXU1FSnOkj3KiGEEEIIIX5QJUqUoHXr1gwePJgZM2YAsGrVKm7dusXUqVM/Gng0a9aMEydOaORdvnyZVatW0bZtW5YuXfrZ9ZCWDiGEEEIIIXQUn4xJF82aNSM2NpZFixap86Kioli6dClly5bF2dk50X0/DDgAtm/fDkDevHl1qocEHUIIIYQQQvygihYtyoMHDwgNDdXIv3DhAgBFihTR6Xj29vYABAQE6LSfdK8SQgghhBAiFTE2NsbExEQjLyoqiujoaK2yDg4OeHt7a+W/zXN0dNTp3MOGDSM2NpYtW7botJ+0dAghhBBCCKEjRVGSLY0YMYKQkBCNNGLEiATraWZmRlRUlFZ+ZGSkevvnat26Nb/++iszZszg0aNHOn1f0tIhhBBCCCFEKjJ58mRmzpypkZdQYAEQERGh1SoCqGefioiI+Kxzurq6snTpUg4cOMDIkSN1rLEEHUIIIYQQQugsORcHjI6OTrArVUK8vb1xcnLSyndwcADAy8vrk8coVKgQu3bt4tatWzRr1oy4uDjdKox0rxJCCCGEEOKHde3aNXLlyoWlpaVGfqlSpdTbPyZbtmwcOHAAPz8/6tSpw+vXr7+oHhJ0CCGEEEII8YPasmULhoaGdOvWTZ1nbGxMp06dOHfuHC9evAAgU6ZM5M6dW2NfOzs7Dh06RHx8PDVr1tR5xqr3SfcqIYQQQgghdKTrehnJ5cKFC2zatInJkydja2vLo0eP6NChA1myZKFLly7qcqtWraJSpUro6emp8w4cOED27Nn566+/cHV1xdXVVb3N19eXw4cPf3Y9JOgQQgghhBDiB+bm5saECRNo37491tbW3Lhxg3r16nHy5MmP7vd2DY9hw4ZpbTt+/LhOQYceJOMomGRUyK5McldB/CTuvnqW3FUQP4l45ad8nItkEHZufnJXQfwkzIo3TO4qJKpupjrJdu69z/cl27m/lIzpEEIIIYQQQiQp6V4lhBBCCCGEjpJzytzUSFo6hBBCCCGEEElKgg4hhBBCCCFEkpLuVUIIIYQQQuhIkck7dCItHUIIIYQQQogkJS0dQgghhBBC6Ci1LA6YUkhLhxBCCCGEECJJSdAhhBBCCCGESFLSvUoIIYQQQggdKbJOh06kpUMIIYQQQgiRpKSlQwghhBBCCB3JiuS6kZYOIYQQQgghRJKSlg4hhBBCCCF0JIsD6kZaOoQQQgghhBBJSoIOIYQQQgghRJJKUUHH3LlzKV68uPqzoaEhzZs3x8bGRqts1apVOXLkyPesnhBCCCGEEIBqIHlypdQoRQUdPXr0IFeuXOrPVlZWrF+/noIFC2qVtbOzo2LFit+zekIIIYQQQogvkOIHkuvp6SV3FYQQQgghhNAgiwPqJkW1dAghhBBCCCF+PBJ0CCGEEEIIIZJUiu9eJYQQQgghREoTL+t06CTFBR1ubm6ULl0aAFNTUxRFoU+fPjRq1Eij3PsDzoUQQgghhBApV4oLOmrUqEGNGjU08j4MON6SlSCFEEIIIURykF+huklRQYeBgUFyV0EIIYQQQgjxjaWooCNdunQEBQUldzWEEEIIIYT4qNS6SF9ySVGzV/n4+LBt2zaaNm2KsbFxcldHCCGEEEII8Q2kqJaOLVu20KBBAxo0aEBoaCjbtm1j7dq1HD16NLmrluKYmZvRsXcbChbNT4Gi+UhrbcXo3yaya+M+jXJN2jagbrOaZM3hgqWVBf6+AVw6c5UFM5bi9dxHXc7E1JgRfw6iYLH82DnaYmCgz3MPT3as38umFVuJjY1LtC5jpg+nabsG/Pfvafq2H6LO/6VsUZZum5vofnMmL2TJPys18kqV/4Vff+tA3kK50dfX4+nj56yYt5aDO4/o+hWJJJQmjTmDBvakRMmilPilCOnTp6PLrwNYvXqzuoyenh7t2jWjcaPaFC5cgPTp0+Hh8YxNm3Yx8++FREVFaRzTysqSEcP70aBhLZydHPDzC+Do0VNMnDST58+91OVGjxrI6NEDteoUGRmJVdocWvm2tjaMHTuYOrWrkSFDOnx8/Dl27DTdewz+ht+I+N7SpDFn8KCelCxRlBIlipA+vTWduwxg1epNWmXz5MnBjGnjKFeuJNHR0ezbf5TBQ8YREPBSo5y9vS1jxwyiWtUK2NtnxMvLl927D/HnlNm8fPlKo2yvnh3p2bMj2bJmJiDgJZs372bMuKmEh0ck6XWLb+POkxfM2bif6w+foihQKGdmBrSpS54sThrl4uPj2XLkPFuOnOWZTyBmJsbkzepEtybVKJIri0bZ6JhY5m4+yN6TVwh5HU7OzA70aVGLMoU0J76JiY1j6Y4j7Dp5Gb+XwdimT0ujiiXo3LAyhu91Mw+PjGLF7uPcfPSMW4+fE/I6gvE9WtCwYgmt69l65Dx7T13B3cuP0PAIMlpb8Uu+7PRoWh2njOm/2fcmxNdIUUFHu3btMDU1pVGjRrRp04a2bdvSoUMHfH19Wb9+PevWrePKlSvJXc0UwTpDWnoM6oLXCx8e3HlIiXLFEyyXp2AuPJ95ceLgKUKCQ3DK5EiTdg0oX70sLap0wN83AAATUxOy587KySNn8HrugxIfT+ESBRkyvh8Fi+VjRK9xCR4/X+E8NGhZh8iIKK1tTx548HvvP7Ty6zWrRdnKpTh74oJGfsNWdRk3cwTnTlxkzuQFxMfF45I9M3aOtjp+OyKp2dikZ9SoATx9+oIbN+5QqVJZrTLm5mYsXfI3585dZvHi1fj5B1K6VDHGjBlE5cqu1KjZQl1WT0+P/fvWkzdvThYuXMWDh0/IkT0L3bu7Ub16RQoVrkRY2GuN4/fuM5zXYeHqz3Fx2oGxs7MDx4/tAGDx4tV4evng6GBHiRJFvs0XIZKNjU16Ro8a+OYevJvgPQjg5OTAsSPbCA4OYdToKVhYpGHggO4UKJCHMmXrEhMTA6iCmFP/7SJNGnMWLFzJ8+deFCqUj169OlKxUllKlqqlnrxk8p+/M2Rwb7Zs3cOcOUvJmzcnvXt3Il++XNSp1/a7fQfiy9x1f0HHcXOxy5CO7k2qoygKG/89Q5fxC1g7sS9Z3vubM3PtXlbv+4+6rsVoUb0soa8j2HLkHF3Gz2fFuN4UzJFZXXb0/I0cvnCDtrXLk9nehl0nLtFn6lIWj+pBsTxZ1eV+n7uef8/foFGlEuTL6szNR0+Zu/kgPoFBjOnaTF3uVehrFm47jINNOnK5OHLpzuNEr+mehydOttZULJ4PqzRmePq/ZNvR85y8cpdNUwZgmz7tN/4WBUj3Kl2lqKADVG8rN2zYwIYNG0iXLh0tWrSgTZs29O/fn/79+/Pw4UPWrFnDunXrcHd3T+7qJht/30CqFKxHoP9L8hXOw/qDyxIs9+fw6Vp5Rw/8x4ZDy6nfvDbL/rcagJCgUNrX7aZRbvOqHYSFhNG6S3Omj51NoP9LrWMNmziAPZv3U7L8L1rbXga8Yu/Wg1r53Qd15unjZ9y+dled55jJnhF/DmL90i1MHT3ro9cukp+3tx+ZMhfF19efYsUKce7sPq0y0dExVKjYkHPnLqvzli1bx9OnLxg7djBVqrhy9OgpAEqVKkaJEkXo99tIFix41/p1/8FjliyeSdUq5dm564DG8bdt20tgoObb5w/NnfsXsXGxlC1bl5cvg77iikVK4+3th1OmIvj6+lO8WCHOn9ufYLnhw/qSJo05JUvXUreYXbx4jYMHNtDBrQVLlq4FoH79GmTJkokGDd3Yt/9dy+qrV0GMHjWQwoXzce3abeztben/WzdWr9lCp86/qcs9fPiE2f9Mol7d6uzZ+28SXrn4WnM3HcTE2IjV4/uQzjINAHVdi9Fg4FRmb9zPzAEdAIiNi2Pz4bNUL1WIP3u3Vu9fvXRh6v42mX2nr6qDjpuPnnHg7DUGtq1Lh3qVAKhfvjhNh85g1rq9rBrfB4Bbj59z6Nx1ujWpRu/mNQFoUb0M6SzTsHrfSVrVKEsuF0cAMqaz4sj80diks+L24+e0GTU70Wsa2aWJVl6VXwrQeuQ/7D55mS4Nq3zltybE10tRYzo+FBQUxKJFi6hUqRKZM2dm+PDhhIeHM378eB4+fMjp06eTu4rJJiY6JsEg4HN4PfcGwDKtxWeU9XlT1lJrW73mtciRJxtzJi/87HMXKJoXl2yZ2LvtkEZ+c7fGGBjoM2/qYkDVfUykXNHR0fj6+n+0TExMjEbA8dbOnargIU+enOo8KyvV/eX3puXtLR8fPwAiIiO1jqOnp4elZeL3cO7c2aldqwozZy7g5csgTExMMDRMce9ZxBf6nHsQoEnjOuzdd1iji96Roye5/+AxzZvVV+dZWaruwQ+P6e395h6MUN2DpUsXx8jIiE2bdmqU2/jmc4sWDb7gasT3dOW+O6UL5FQHHAAZra0onjcb/125S3ikquU+Ni6eyOgY0n/wtzK9lQX6enqYGBup8w6fv4GBvj5Nq5RW55kYG9G4UkmuP3yKT2CQ6tz3ngBQq0wRjWPWKlMERVE4eO66Os/YyBCbdFZffJ2OGa0BCA3Xfn6Kb0NRlGRLqVGKDjre5+XlxfTp0+nQoQM7d+5ET0+PUqVKJXe1Uo201lakt7EmX+E8jJ81EoDzJy9plTM0MiRd+rTYOdpSpXYF3Hq2xvO5N8/dX2iUM09jTv9RvVj6zyqdgp86TVRvdvZ90AJSqsIvuD96imvVshy6soNzT47w390D9B7aFT09PV0vV6RgdvYZAQh8rz/95cvXCQt7zbhxg6lUqSyOjvaUL1+ayX+O5OLFaxw5clLrOPfvnSEw4B4vA++zYvlsbG1tNLZXqVIeUAUyBw5sIDTkMSHBj9i1azUuLs5JeIUipXB0tMfOLiOXL1/X2nbx4jWKFCmg/nzy1Dni4uL4e+Z4SpUshpOTA7VrVWHE8H7s2Lmf+/dVXVtMTFSTnLwNQt56O5ajWLFCSXU54huJjonVCBjeMjM2IiY2jkdvXraZGhtRMEdmdp24xN5TV/AOeMWDp16MWbARqzRmNKvy7jfIPQ8vXBxssDA31ThmgRyZ1NsBYmJU3UA/PL/pm/vqzhPNv7W6Cgp9TWBwGLcfP2fMAtX4plL5tce6CZEcUsVrv0yZMtGmTRtat25NgQIF0NPT48yZM6xduza5q5Zq/Ht1JyamJgC8CgxiysiZnPvvola5anUq8dfC8erPt67dZWz/SVr95bsP6kRUZBSrF2347Dro6+tTs2FVbl65zXMPT41tmbNmIj4unvGzfmfFvLXcv/2IqnUq0m1gJwwMDZj95wJdLlekYIMG9SQ4OIQDB4+p8wIDX9G2XU/mz5vKoYPvBgIfPHScVq26adx/r4KCmTtvOefPXSYqKppyriXp2aMDJUoUoXSZOoSGhgGQI4eqD/W8eX9x6fJ12rTpQabMTowaOYD9+zdQvHg1rR+O4sfiYK/qm+/9psXsfT4+vmTIYI2xsTHR0dHcvfuQHj2HMvWv0Zw+tVtdbuWqTXTr/m7SgQdvgo+yZUtw/MQZdX55V9UPUCdH+yS5FvHtZHHIyI1Hz4iLj8dAX/XuNSY2lpuPnwHg9zJYXfbP3q0ZOnsNv89dr85ztk3Pyj9642yXQZ3nHxSSYKvE2zz/V6pjujiqXrpcu++Os+27Ad5vW0D8XoV81bVV7z2R6JhYANJZmDOsQ0OtgexCJJcUG3RkyJBBPZ6jTJky6Onpce/ePcaMGcPatWt5+vRpclcxVenddhDGJsZky5mFuk1rYmaWcPelC6cv0615PyzTWlKqfHFy5cup1dXJJVsm2v7agmE9xxITHfPZdShV/hdsbDOw9J9VWtvM05hhYGDArInzWP6/NQAc2XuctNZWtPm1BUv+WUX463Ct/UTqMmxoH6pVrUCfviMIDtb84+rv/5Jr128zf/4K7tx9QOFC+Rk0qCdLFs+kdZse6nL/+99Sjf2279jHpYvXWLXqf/To3oFp01UzplmkUXWd8PX1o2FDN3VztOcLb9asmUerVo1Zvnw94sdlZqZ66xwVFa21LfJNFxozM1Oio1Xbvbx8uHjxGvsPHOXpsxeUL1eKPn06ExjwkqHDJwBw9dotzp+/wpDBvfD09Ob4iTPkzZOT/82ZTHR0tPqcIuVqUb0sk5ZtY9zCTXSsX5l4RWHx9sP4vwoFIPK9v2tpTE3I7mxHoZwulCqQg8CgUJbtOkb/GStZPrYX1laq50xUdAzGRto/qUze5EVFqwKB8kXy4Ghjzcy1ezA1MX4zkPwZ/9t4AEMDfaJ0+JuakLnDuhAdE8sTT1/2nrpKRAL3vvh2ZCC5blJU0GFubk7jxo1p06YNVatWxcjICG9vb2bNmsXatWu5evVqclcx1bp4WjXr1+mj5zh24CRbj68hPDycDcu2apR7GfBK3e3q8J5jdOnnxsJNs6hfpqW6G9XQif25dukmR/Ye16kOdZrWIDY2lgM7D2tti4qMwjyNOfu3aw7A3L/9X1yrlCFPwVxcOXdNp/OJlKV5s/r88cdQli1bz6JFqzW2Zc2amX8PbaJz5/5s36EalL579yGePn3O0qWzqFmzMgffaxn50IaNO/jrr9FUqeqqDjrejgPZsmWPRv/XLVv3sHz5P5QpXVyCjh/c25ast12i3mf6puX3bZmyZX5h546VlHOtz+UrNwDYtesgIaGhjB41kOUrN3D37kMAmrfsyvq181m65G8AYmNjmfXPIiqUL02uXNmT/LrE12lRvQy+L4NYsfsEu/5TjTvLn82ZTvUrsXjHEczf3BuxcXF0+3MRv+TNzohOjdT7lyqQkyZDZrBiz3EGtKkLqLpLvW1heF/UmzwTY0N1uTlDOzPknzUM+lv1As7YyJD+beqyZPsRzE2/bo2ykm+6UrkWyUPlX/LTdMgMzE1NaF2z3FcdV4hvIUUFHX5+fpiamhIWFsa6devUa3Sk1gEzKdWLp57cu/WAOk1qagUdHzq85xj9fu9B5Vrl2bJ6JyXLFce1ShkGdBqOY6Z33QgMDQwwMTXBMZM9wa9CNKYyBdU6IFVqV+T8f5d4GaA945C/TwAu2TNrjQ95W9YqgYHsIvWoWrU8y5bNYv/+I/TuM1xru1v7FpiamrB3n2ZAunuPKggtW+aXjwYdAC9eeJPeOp36s7eXql+2r5/mwOD4+HgCA19h/V5Z8WN6263qbTer99nb2xEY+ErdytG1a3t8ff3VAcdbu/ccYuyYwZQp/Ys66PDy8qFi5cbkyJEVe7uMPHzkjq+vP888LvPw4ZMkvirxLfRtWZsOdSvy6IUvluam5MzswOwNqhnQXBxUXaAu33Xn0XMfBrerr7Gvi0NGsjnZcu2BhzovYzor/F4F86GAIFWLbkbrd1PW5shkz7Zpg3j8wpeQ1xFkd7bDxNiI6at28UvebN/sGjPZ2ZAnixP7Tl+RoCOJKNLSoZMUFXQcPnyYtWvXsmvXLq2Fw95naGhI3bp1adeuHc2bN/+ONfxxmJqaYGSiPZDuQ2/HgVhYqWbvsHe2A+Dv5VO0yto52rL/4jamjp7F2sWaC3RVqlkeC8s07Ptg1qq37ty4j0v2zNjaZ8Tz2btZZmztVYODX31ialSRcpUoUZTNm5Zw+fINWrfpmeB6GrZ2Nujp6WFgoDm3hdGbrgmfM+uUi4sz167fUn++cvUmAE6ODh8c0wgbm/T4BwTqfC0idfHy8sHPL4DixQtrbStRogjXr99Wf7aztcHgvYXZ3jIyVD0nE7oHHz1y59Ej1dTtefPmxNHRnlXvLZApUjYrC3ON9TPO3XqIXfq0ZH0z7uJlsKq7VXx8vNa+sXFxxMW9y8+dxZGLdx4TFh6pMZj85iPVOJE8WRw19tfT0yPHey/uTl69S7yiUKpgTr6lyOgYYmK1W2CESA4pavaqRo0asXnz5kQDjgoVKrBw4UJ8fHzYtm0bNWvW/M41TF0MDAwSnOq2QNG85MibjTvX76nz0iWycFCTtqo3PHeuqcpeOHWZ/h2Ha6WXAa+4de0u/TsO58Qh7amMazeuQUR4BEf2nUjwPG9XHG/c5t0bJT09PRq2rEvQy2Du3Lj/mVctUpI8eXKwc8dKnj59TqPGHYlMYOpbUK1xoK+vT7Nmmm8UW7ZsBMC1a++CCRsb7dV1u3d3w9bWhkOHjqvzTpw4i6+vP61aN8LExESd7+bWAkNDQ44c/u8rrkykFtu276NunWo4O7/70Velsiu5c2Vny9Y96rwHD59gb29LxQplNPZvlcA9+CE9PT2m/DmK16/DWbhIe8yaSPkOnL3G7cfPaVu7PPpvBpe/bfE4cPaaRtm77i/w8PLXCCSqlSpEXHw8W4+eU+dFx8Sy88QlCubIjH2GdImeOzI6hrmbD5IxnRW1yxbRue6xcXGEhGmPebz56BmPnvuQL5vM1idShhTV0pGQggUL0rZtW1q3bo2TkxM+Pj5s2bKFXbt2ceTIkU8f4AfWqnNTLK0syfimNaBi9XLYOai6Eaxfuhk9PT0OXdnOwZ1HeHzfnYjwCHLmzU7DVnUJC3nNopnL1ceq27Qmzd0ac+zAf7x46kUaC3PKVipFmUolOX7wJBdOq/q9+nj64uPpq1WXIRN+46X/S44d0P4hZ5XOEtcqpTm89zgRb6aV/NCxA/9x7r+LdOnXHuv0abl/5xFValWgWOkijB/8l04D1sX30bNnR9KltcLBUdX6Va9udZydVK0Kc+ctJz4+nj2712JtnZaZMxdQp3ZVjf0fP/Hg/HnVWKNVqzYxoH935s2dQpEiBbhz5wFFixagc6fW3L59jx073y0M+OjheTZv3sWt2/eIjIyiXNmStGjRgGvXbrF48Rp1uejoaEaMmMiyZf9w9MhW1q7bSqZMTvTt05mTJ8+xfUfCi8mJ1KNXz46kS5cWB4c392C9ajg7q+7B/81dRkhIKFP+mkOzpvU4fGgTc/63FAuLNAwa2IMbN++wYuVG9bHmzV9Bxw4t2bF9BXPnLefp0xdUqFCa1q0a8++/J7hw8d2Ywpkz/sDU1JTr129jZGRI61aNKVGiCJ269NdYD0SkTJfvPmHhtn8pUzAXaS3TcPPhU3aeuES5wrlpW9tVXS5fNmdKF8zJrv8uExYRRZmCuQgICmH9wdOYGBvRrnZ5ddlCOTJTo1QhZm/Yz8vgMDLZ27D7v0t4+b9kXDfNHhlDZq0mo7UV2ZzteB0RyY7jF3nh95L/De1Mmg8mIlh/8DShryPwfzOr1Ykrd/ANVHXjal2rHJbmZoRHRlOjzyRqlilMdmc7zEyMefjch50nLmJhbkq3xtWS6qv86Un3f93oQcrrkPZ2ity2bduSL18+/P39OXbsGC1atKB58+Zs3779q89RyK7MpwulcPsubsUpk0OC22qXaIKfTwADRvemRLliOGZywNTUBD/fAM7/d5HFs1aoF/4DyFc4Dx17t6VgsfxksLEmLi4Oj0fP2Lv1IOuXbkmwS8yHdXl87wl92w/R2tasfUNGTxtGv/ZDOPFv4gs6mpmb0Wd4N2o2rEradFZ4PH7G8v+tSbRLVmpx99Wz5K5Cknhw/yxZsmRKcFvOXKoFsh4+OJfgdlAFGr92Haj+7Ohoz9gxg6lYqQxOjvYEBr5i3/4jjB49RWPl8fnzplKmTHGcnR0xNTXh6TNPdmzfx+QpswkLe611nhbNGzBkSG9y585OUFAIW7ftZfToKQmWTe3if7I/gI8enEv0HsyesxRPn6rWPMiXLxfTp46lXLmSREdHs2//EYYMHY+fn+ZilLlyZWf8H0MpWaIo9vYZ8fLyZevWPYwbP11jemW39i3o1+9XcmTPQnx8PBcvXmPylNkaU+j+6MLOzU/uKnyx574BTFq2nXvunryOjMIpY3rqVyiOW90KGH3QjS4yOoaVe45z4Mx1vPxfYmRoQNE8WendvCZ5sjhplI1602Kx99QVQl5HkDOzA72b16Rc4dwa5ZbvOsbOE6qAxMTYiGJ5stKzWQ2t4wHU7vsnXgmMgwTYN3sEThnTExMby99r93LxzmO8/F8RGR2DrbUVpQrmpGvjqjhl1G4dTk3MijdM7iokqri966cLJZHLPqeS7dxfKkUFHd26daNt27aUK1eO4OBgtm3bxoYNGzh69CjZsmXjwYMHNGvWTIIOkar8qEGHSHl+tqBDJJ/UHHSI1CUlBx1F7ZNvgP5Vn8Rf4qZUKap71fz583F3d6dp06bs3buX2PcGP0kTlhBCCCGEEKlTihpIfunSJbJmzcr8+fOZMWMGpUuXTu4qCSGEEEIIoUVRlGRLqVGKCjpKlSpFrly5WLRoEbVq1eL06dO4u7szefJkihQpktzVE0IIIYQQQnyBFBV0ADx+/Jhx48aRO3duypQpw+7du+nUqRObN29GURSaNWtGmTK6jccwNjbG0tJSIxkZf3qNCiGEEEIIIcTXS3FBx/suXLhAv379cHR0pF69eqxfv5769etz8uRJfH19WbZs2WcdZ8SIEYSEhGikLv3ckrj2QgghhBDiRxWPkmwpNUpRs1d9DjMzM5o0aULbtm2pUqUKpqamn9zH2NhYY3EwgFzpisraD+K7kNmrxPcis1eJ70VmrxLfS0qevSo5Z0K94Xs22c79pVJUS8fJkyf5888/qVu3LunSpUuwTEREBGvXrqVOnTo4O3/eKpvR0dGEhoZqpNQecJiZm9FzSBfmrZvJf3cPcN3nDA1a1tEqV6BoXn6fMpj1B5dx6fl/XPfRbR55PT09mrs1YuPhFZx9fJijN/cwd90MCv9SQKNc9txZmbZ4InvPb+bck6Mcv72PZdvnUbG69nRylWtVYMfJ9Zx6cIjZq6aS0c5Gq8w/K/9i9LRhOtVVfD9p0pgzZvQgdu9eg4/3LaKjXtC+ffNP7wjY29syaeIIDh3cRGDAPaKjXlChwqcf3GnTWvHi+TWio17QpHFdjW2Ojvbs2LGSAP+7XL92lLp1tRfDatSwNi+eX8PKyvLzLlKkGMbGxkz+83eeeVwmNPgRZ07tplrV8p/cL1eu7MyYNo6TJ3YSFvKY2GhPXFwS/7tRr151Lpw/QFjIY548usDYMYMwMDDQKJM3b06OH93Gq8D7nDu7j9Klimsdp/9v3bh+7ajWviLleOrtz9DZa6jeeyKlOvxOw0FTWbD1XyKiojXKxcTGsmTHERoOmkoJtxFU7v4Hff5aim9g0CfPERgUyugFG6nUfRwl3UbQcsQsDp27rlXuyMWb9Ji8mGo9J/BL++FU7z2RQX+v4uF7a2mBatDygi2HqNZrApW6j2Pqyp3EvDfLJ0B4ZBTVek1g3+mriKSnJOP/UqMUFXRkzpyZYcOGsXPnTvz9/bl58ybz58+nbdu2uLi4aJUPCAhI4Cg/B+sMaekxqAtZc2XhwZ2HiZZzrVqWJm3qo6Dg+dRT5/MMHNuHUVOH8ujuE6aPncOqBetxyZaZpdvnUaBoXnU5B2d70qQxZ9em/UwdPYtFf68AYPbqaTRt9+4thVNmR6YuHM+ta3f4Z9J8XLJlYvyskRrnLFupFMVKF+F/UxbqXF/xfdjYpGfUqAHkyZ2DGzfu6LRvrlzZGTKkN05O9ty6de+z9xs7djDm5mYJblu69G+yZc3M7yP/5OrVW6xft0Djx6WJiQlT/hrF2HHTCAkJ1am+IvktW/o3/X/rxvr12xkwcCxxcfHs3rWacmVLfHS/0qWK06dPZyws03D33qOPlq1VszLbtiwjOCiE3/qPZueuA/w+4jf+mTVRXUZfX5/Nm5ZgYGDAsOET8fcLYNvWZVhaWqjLZMyYgVEj+zN48LhPLqoqkodPYBBtR8/h5sNntKpRliFuDSiU04X5Ww4xfM5adbmY2Dj6/LWMxTuOUK5Qbn7v1JiO9SthZmJMaHjkR84AYeGRdPxjHkcu3KRZ1dIMbFePNKYmDPlnjVZA8PCZD1ZpzGhT25XfOzehRbUy3PPwpN2o2dx/+m6F+72nrrBk51EaVy6JW92K7DhxkVV7/tM41uLtR3DKaE2dckW/wTclxLeVotbpcHFxwcnJCVdXV1xdXSlbtixdunShW7duKIqCl5cXp0+f5tSpU5w6dYrr17XfGPws/H0DqVKwHoH+L8lXOA/rDyY8vmXTim0s/99qoiKjGfHnQLLk0A7eEmNgYEBzt8Yc2n2UkX3Hq/P/3X2UfRe2UqdJTW5dvQvAqSNnOXVEs6lvw7ItrD+0jPbdW7F1zU4AylYqia+3P6P6TgDA/aEHi7fMwdjEmOioaAwMDBg8vh+LZi7n1We8SRLJw9vbj0yZi+Lr60+xYoU4d3bfZ+975coN7OwL8OpVEE0a16VMmV8+uU/+fLnp3q09kybNYtw4zVXvTU1NqVypHNWqN+fUqfMsWrSa0mWKU716RZYsUf2AGDigOyHBoSxbtk63CxXJrsQvRWjVshFDh41n5t+qFxGr12zh+tUjTJk8ivIVE+96sXvPITJkzEtY2GsGDuhO0SIFEi3711+juXHzLrXqtFYHC6GhYQwf1pc5/1vC/fuPyZkzK3ly5yBr9hI8f+7F6jWb8fW+RZnSxTn07wkAJk4YzslT5/n38H+Jnkskrz0nLxP6OoIVY3uRI5M9AM2qlkaJV9h98jIhYeFYWZizZt9/XLr7hBXjelEwR2adzrHlyDme+QSwaGR3ShXIAUCLamVoN+Z/zFizm+qlCqpXP+/RtLrW/k0ql6RGn4ls+vcso39tCsB/V+9Sp1wxejevCahWQD9+5TZdGlUBVCutrztwimVjen7ZFyNEEktRLR0Anp6ebNy4kb59+1K8eHGsra2pVasWEydO5P79+9StW5d//vmHy5cv8/Lly+SubrKJiY4h0P/T1/8y4BVRkdGfLJcQQyMDzMxNefnBeV4GvCIuLo6oyKiP7h8fH4+vlx+Wad+9BTQxNSE0+N2b5uBXIejr62Niqhpz06pzUwz09Vm/dPMX1Vl8H9HR0fj6+n/RvmFhr3n1KkinfWbM/IOdOw9w6vQFrW2mpibo6+sT9CpYnRccFKJuFXF0tGfIkN4MHDQ21c5t/jNr2rQusbGxLF7y7g10VFQUy1dsoEyZX3B2dkx031evgggLe/3Jc+TNm5P8+XKzZMlajdaJ+QtWoq+vT9Mm9QAwMzN9c1zVvRYREUlERCRmb+61okUK0KZ1YwYP+UP3CxXfTViE6m9XhrSaXS1trK3Q19PD0NCQ+Ph41h44RZUSBSiYIzOxcXFaXa8+5so9d6yt0qgDDlC1lNUsXYiAoFAu3Xny0f3Tp7XA1NiY0PAIdV5UdAxWad619lpZmBMZ9a6r+Iw1e6hZpgj5s2f67HqKrxOvKMmWUqMUF3R86PXr1/z777+MGzeO9u3b0717d86dO4eenh5WVlbJXb0fWlRkNDcu36JByzrUaVIDeyc7cubNzvh/RhESFMqW1Tu19jEzNyVd+rQ4uzjRrltLylUpzfmTl9Tbb1+7S56CuajVqDpOmR3o2r8DT588JzQ4FOsM6egxqDPTx80hNla6JQiVpk3qUqb0L4z4fVKC24OCgnn02INhw/qQJUsmWrdqTOHC+bl48RoAk/8cycGDxzh16vx3rLX4VooULsCDh08IDQ3TyH/771ukcP6vP8ebFpDLlzVbz729fXn+3IsiRVTnePDgCUFBwYwdPYjMmZ0YNLAHVlYWXL16E4BZf09g3rwVPH7s8dV1EkmnRN5sAIxbtIl7Hp74BAZx4Ow1Nv97lja1XDE3Neaxpx/+r0LIldmB8Yu3ULrjSEp3HEmzoTO4cPvjXfUAomNjMU1gan5TY2MA7ri/0NoW8jqClyFhPHzmzbhFmwmLiKRU/pzq7fmzZeLAmWvcePiUh8+82XL4HAXeBBhnbzzgwu1H9GtV64u+EyG+hxTVvepD+fPnx9XVlXLlylGuXDlcXFyIiori6tWrzJgxg9OnTyd3FX94v/f+g6kLJzB53jh13nMPTzo06IHnMy+t8oPG9qV5h8YAxMXFcWTfCSb/PlO9/eqFG6xfuoW/FqjeBAa9DGZwV9WYjj7Du3Pjym1OHtZtsLv4cZmamjJlymhmz17M06cvcHFJ+A1er15D2bB+ES1bNgLgn9mLOXv2EqVLF6dhw1oUKlzp+1VafFP2Drb4ePtp5Xv7+ALg4GD31edwsLd9c0zt8/j4+OLooOqCEx4eQe++I1i8cAYDBnQnNjaWEb//ybNnnrRq1Yjs2bNQr0H7r66PSFrliuShd/OaLN15lOOX341J69qoKn1aqn60P/NRteSu2fcfVhbm6i5OS3YcpdeUJayb2I9cLom3smVxyMj5mw/x8n+FY0Zrdf6Ve+4A+L0M0dqn/Zg5eHipzmtuakLXxlVpXPnduKW2tV05c+MB7cf8D4Dsznb0aFaD2Lg4pq7axa+NqmKTTl7Gfk+pdUB3cklRQUeFChUoV64crq6ulC5dmnTp0uHr68uZM2eYO3cuZ86c4fLly8TEpO6Zp1KT12HhPH7gzo3Ltzh/8hIZbDPQuW97Zi2fQqdGPQl6GaxRfs3iTfy75xgZ7W2o2aAqBvr6GBlp3mZTR89i1YL12Nim5/F9DyLCI8idPyf1m9eiRbWOWFim4ffJgylRrhhP3Z8zadg03B8+/Z6XLVKIoUN6Y2RkyJS/5ny03PHjZ8ieoyT58uXG29uHFy+80dPT4++Z45k1ayHPnnnSrVt7+vbpgp6eHv/MXszixWu+01WIr2FmakpUlHZXzsg33Tvfdnn6qnOYqbqsJHYey/dmPNu4cScHDx4nd67suHs8w88vADMzUyZPGsnoMX8RFvaa0aMG0L5dc8Jev+aP8TPYufPAV9dRfFuOGa0plicr1UoWIq2lOSev3mXJzqNkSGdJ65rlCH/TLfl1ZBQbpwzAPkM6AErmz0G9/n+xfPdxJvdpk+jxm1QuyebD5xjyz2qGuDUgQ1oLDp29wdFLtwCISuB3zPjuLQiLiMLTL5AdJy4RFR1DXLyC/ps+KWnMTFk2pgfuXv7ExsWR3dkOQwMD1h44RUxsLO3rlOfxC1/+XL6dp97+lMiXnZGdm2Bh/vX/jQjxLaSooOPYsWPExMSwefNm+vbty9mzZ3F3d0/uav20DAwMWLR5NpfOXGHKyL/V+ef/u8i2E2vp2KstsybO09jH49FTPB6pAoQ9mw+wYMMs5qyeRtvav2qU8/H0xcfTV/152MQBbF61A49HT/nzf2Oxc7Llt47DaNCiNrNXTaORa2uZCeYn4+LizMCBPej320hevw7/ZPnXr8O5ePHdrDAdOrTEzs6WqdPmUqWKK1Mmj6Jjp34oisKqlf/jwYMnnDghrWopXURkpNY6S6AaywOqcRVffY4IVb/5xM4T+cE5goKCOX/hivrz8GF98fMPYMXKjXTq2IpuXdvj1qEvLi7OrF87n4KFK0uXqxRk/5lrTFiylV0zh2L3JpioVrIg8YrCrPV7qV22iLprVJFcWdQBB4CDjTVFc2fh+idehOVycWRK3zZMXLKVDmPnAmCTzpIh7Rswadk2zE2MtfYpnCuL+v/XKlOERoOnATCoXX11vr6+Ptmd37XuvQp5zYIth/ijewtAj77TllGhaF4GtqnL9DW7mbJiBxN7tdLl6xE6SK1jK5JLihrTcfPmTQwMDGjdujVDhw5lyJAhtGnThixZsiR31X5KxUsXIWfe7Bw/eEoj/5n7C5489KBIiYKfPMa/e45RoGg+XLInPvNHzYZVyZrLhQXTl6Kvr0+NBlWYP20Jd67f4+8Jc7FzyEih4l/fb1ukLmPHDMbTy4f//juLi4szLi7O2NtlBMAmY3pcXJzR09NLcF9LSwvG/zGUUaMmEx4eQcsWjdi2bS+7dh1k9+5DbNu2l9atGn/PyxFfyMfbD3sHW618B3vVDy9vb1+tbbp6263qbTer99nb2+Hl7aOV/5aLizMD+ndn4MAxKIpCq5aNWLxkDceOn2bFyo2cO3eZli1S7uJmP6NN/54hTxZHdcDxVqXi+YiMiuGehycZrVXdlD4cbA6qQd4hryO08j9UvVQhDs8fzdqJ/Vg9vg8H5vyOs116AFwcMn50XysLc0rmz/HJ9Tbmbj5A3qxOVClRgJuPnhLwKoQBbeqSP3smejarwYGz14iPj/9kXYX4HlJUS0eRIkWwsLCgTJky6nEc7dq1w9zcHD8/P86cOcPp06fV3axiP1gUR3xb6TOqHo76BtqxqaGRIQaGn1746u2sVJZWaRLcbmpmwoDRvZn312JCQ8JIb2ONkbER/j6qNViiIqMJCQ7F9hMPaPHjyZTJiZw5svLgvvaqq/+bMxmAjLb5CA7W7hs9cmR/PDyes279NgAcHO24fu2Weru3ty+FvsEAZJH0rl+/TaVKZbG0tNAYTF6ypGodgmvXb3+TcwAUL16Yi5euqfMdHOzIlMmRJUvXJrInTP1rDLv3HOL0mYuqfRzt8PJ6Fwh5efvi6Gj/1XUU305gcJjGLFBvxcaqfpzHxcWTM5M9hgYG+L0K1irn/yoEa8uE/6Z9yMjQUD3YG+D8TdW6WqUK5kxsF7XI6BjCPrIeyP2nXuw4fpH1f/YHwO9VCFZpzDF500pja21FTGwcr0JekyGdLIoqkl+KaukACAsLU89WVb16ddKlS0fJkiWZPHkyMTExDBgwgNOnTxMcHMyJEyeSu7o/lCw5XLB3etds+/TJMwBqNdJc3TlPwVxkyZ6Ze7ceqPPS21jzIUNDA+o3r0VEeCSP73skeM5OfdoREhzK1jW7ANUUujExser1RNKlT4t1hnQE+AV+1bWJ5GFvb0vu3NkxNNT9/cbYcVNp1ryLRho7dioA06fPo1nzLgl2u8qZMyu9enZk4KCx6jw/X39y5343dWWePDnwTWDQsEh5tm7bi6GhIV1/bavOMzY2poNbS86fv8KLF6oJLTJlciR37uxfdI47dx5w995Dfv21Lfr67/4s9ujuRnx8PFu37Ulwv0oVy1K7VhWGj3i3gKCfrz95NO61nPj6yr2Wkrg42HDPwxMPb81pv/efuYq+nh45MzuQxsyU8kXzcP3BU9w93/37PfH05fqDp5R5L2iIiIrG3dOPVyEfn575qbc/m4+co0KxvGR570VaYHCYVllP/5dcuP2IfNmctba9NXXlTppULknON2uNZEhryavQMILDwt/U1Q9DA33SJfLST3w9WZFcNymqpSMh8fHxXLlyhStXrnDs2DHKly9P27Zt1a0hP7NWnZtiaWVJRnsbACpWL4fdm24I65duJiz0NQ7O9tRrppqNI1/hPAB07d8RAO8XPuzZ8m6A485T67l45gq/NukDwN0b9zl7/AINW9bFwiINZ09cwMYuA607NycqMoo1izap9x09dShpLNNw+dw1/Lz9sbHNQJ0mNciWKwvTx84mIly7KdreyY4OPdvSt/1gdfNvXFwcxw+eZOiE33BwtqNK7Yr4+wRw/dItrf1F8urZsyPp0lrh4KgKVOvVrY6zkwMAc+ctJyQklIkThuPm1oKcuUrz9Om7KSJHDO8HQL58uQBo26apenXpyVNmA3DmzZvj9wUFqVo1Ll26zq5dBxOs17Rp49i8eTeX3ntjvW37XrZuWcaE8cMAqFu3Oo0ad/zSSxff0YWLV9m8ZTeTJo7A1taGR488cGvfnCxZnOnWfZC63Ipl/1CxYlkMjZ3UeVZWlvTp3RmAsm8WoezdsxNBwSEEBQUzb/4KddnhwyeyfdtyDuxbz8ZNO8mfPze9e3Vi6bJ13EtgNXN9fX1mzPiDGTPn8/z5u5n8tm7by5TJo/APCCRzZmcKFsiDW4c+3/prEV+hY71KnL52n07j5tGqZlnSWaThv6t3OXXtHk0ql8Q2fVoA+raszflbj+g6cSGta6l+b6w/cBorCzO6NKqqPt6tx8/5dcICejStTs9mNdT5jQdPo3qpQtjbWOPl95JNh8+SNo05o7o01ahPs6EzKFUgB7ldHLFKY84zH3+2H79IbGwcv7Wuk+A1HDp3nQfPvJkxwE2dVzinC+nTWjJ41mqqlizAyj0nqFKiIAb6Ke79svhJ6UHKDJeMjY0pVaqUenXy0qVLkzat6kEQFRXFxYsXOXXqFKNGjfqi4xeyK/Mtq5ss9l3cilMmhwS31S7RBK/nPvxStihLt81NsMz7AQbAdZ8zWnkmpsZ06NmGWo2q4ZjJkZiYGK6ev87cvxZz//ZDdblaDavRqE09cubNTlrrtISHhXPnxj3WL93CiUOaY0LemrpoAgb6+gz6daRGfnoba8bOGM4vZYvx7Mlzxg/5i7s37n/295LS3H31LLmrkCQe3D9LliwJT2H7NshYsnhmgkFHdJT2HPVvGZsk/mavQoUyHP53M61adWfb9r1a22vVqsLaNfPIX6ACPh+0ZAwZ3JtevTqhpwdz/reUGTPmf+oSU50fdVCjiYkJ48cNoU2bJlhbp+XmzbuMHTdNvQo4wJF/N2sFHS4uzjx+mPD6LB4ez8mRq7RGXoMGNRk9aiB58+TA3/8lq1ZvYsLEvxPsytujeweGDe1DvgLlNQazGxgYMOXPkbRr14zXr8MZN346a9Zs+dqvIMUJO5e6//u5+egZC7b+yz0PT4JCw3GyTU+DCsXpWL8Shgbvug7fdX/BrHX7uP7wKfr6epTMn4MBbepqjMm4eOdxgkHHsNlrufbAg8DgUNJZpqFS8Xz0bFaTDO8tmAswf8shTl69y3PfQMIjo7C2sqB4nmz82qgKOTNr/42PjI6h8aBptK9bgTa1XDW23X78nAlLt/LMJ4Bf8mZnXPfmpLey0DpGamJWPOWOicppUzzZzv0w4HKynftLpaigo0GDBuogo2jRohgZGaGnp0dgYCBnzpzh1KlTnDp1ikuXLn31eI4fIegQqcOPGnSIlOdHDTpEypPagw6RekjQkbDUGHSkqO5V27dvB8Dd3Z2NGzeqg4x79+4lc82EEEIIIYQQXypFBR0tW7bk1KlT+PgkPj2hEEIIIYQQyS21DuhOLilqdNGWLVsSDTgyZMjA48ePKV26dILbhRBCCCGEEClTimrp+BgDAwOyZMmCmZn23NpCCCGEEEJ8TzKOTjcpqqVDCCGEEEII8eNJUUFH+fLlsbGxSe5qCCGEEEII8VGyOKBuUlTQcezYMapXr57gtrCwMP744w+ePHnynWslhBBCCCGE+BopakyHnp5eotvCw8MZP378d6yNEEIIIYQQ4ltIUUGHEEIIIYQQqYGixCd3FVKVFNW9CkCRmQCEEEIIIYT4oaS4oGPNmjXExsZ+VoqJiUnu6gohhBBCiJ9QPEqypdQoxXWvOnz4MA8ePEjuagghhBBCCCG+kRQXdKxcuZL169cndzWEEEIIIYQQ30iKCzqEEEIIIYRI6WQcsm5S3JgOIYQQQgghxI9FWjqEEEIIIYTQUWod0J1cUlTQYWBgkNxVEEIIIYQQQnxjKSroEEIIIYQQIjWQMR26kTEdQgghhBBCiCQlQYcQQgghhBAiSUn3KiGEEEIIIXQUL92rdCItHUIIIYQQQogkJS0dQgghhBBC6EiRKXN1Ii0dQgghhBBCiCQlQYcQQgghhBAiSUn3KiGEEEIIIXQk63ToRlo6hBBCCCGEEElKWjqEEEIIIYTQUbwMJNeJtHQIIYQQQgghkpS0dAghhBBCCKEjGdOhG2npEEIIIYQQQiQpCTqEEEIIIYQQSUq6VwkhhBBCCKGjeOlepRNp6RBCCCGEEEIkKWnpEEIIIYQQQkcykFw30tIhhBBCCCGESFISdAghhBBCCCGSlHSvEkIIIYQQQkeyIrlupKVDCCGEEEIIkaSkpUMIIYQQQggdyUBy3UhLhxBCCCGEECJJSUuHEEIIIYQQOpLFAXUjLR1CCCGEEEKIJCVBhxBCCCGEECJJSfcqIYQQQgghdKTIlLk6kZYOIYQQQgghRJKSlg4hhBBCCCF0JAPJdSMtHUIIIYQQQogkJUGHEEIIIYQQIklJ9yohhBBCCCF0JCuS60ZaOoQQQgghhBBJSlo6hBBCCCGE0JFMmasbaekQQgghhBBCJCkJOoQQQgghhBBJSrpXCSGEEEIIoSMZSK4baekQQgghhBBCJClp6RBCCCGEEEJH0tKhG2npEEIIIYQQQiQpaekQQgghhBBCR9LOoRtp6RBCCCGEEEIkKQk6hBBCCCGEEElOkSTpc5KxsbEyduxYxdjYONnrIunHTnKvSfpeSe41Sd8ryb0m6WdPem/+jxCfZGlpSUhICFZWVoSGhiZ3dcQPTO418b3IvSa+F7nXxM9OulcJIYQQQgghkpQEHUIIIYQQQogkJUGHEEIIIYQQIklJ0CE+W1RUFOPGjSMqKiq5qyJ+cHKvie9F7jXxvci9Jn52MpBcCCGEEEIIkaSkpUMIIYQQQgiRpCToEEIIIYQQQiQpCTqEEEIIIYQQSUqCDiGEEEIIIUSSkqDjB9ShQwcURSEiIgJHR0et7ceOHePmzZsaeYaGhvTt25cLFy4QEhJCaGgoFy5coG/fvhgaGmodw93dHUVRmD17tta2ihUroigKTZs21apTYqlUqVLf4MpFUvjw3y4iIoL79+8zZ84cbG1tE9ynefPmKIpCo0aNtLZdu3YNRVGoVKmS1ranT59y+vRprfzz58+jKAo9evTQyN+5cyevX7/GwsIi0fqvWbOGqKgo0qdPD/DR+3D+/Pkf+SZESpcvXz5Wr17NixcviIyMxNPTk9WrV5M3b16Nch/e0zExMbx48YLly5cn+MzU09Ojffv2nDt3jsDAQEJCQrh//z4rV66UZ1cq9LFnwPupYsWKGvvJc02Ir6P9a1L8MExNTRk+fDj9+vX7aDlzc3P27t1LpUqV2L17NytWrCA+Pp5atWoxe/ZsmjRpQt26dQkPD9fat2vXrkyePBlvb+/PqtPo0aNxd3fXyn/06NHnXZRINm//7UxNTXF1daVnz57UqVOHAgUKEBERoVH21KlTALi6urJjxw51vqWlJQUKFCAmJoZy5cpx/Phx9TZnZ2cyZ87Mhg0bNI6VI0cOSpYsibu7O23btmXBggXqbWvXrqVBgwY0btyY1atXa9XZzMyMhg0bcuDAAV6+fKnOP3ToEKtWrdIq/+DBA52+E5FyNG7cmPXr1/Py5UuWLl2Ku7s7WbJkoUuXLjRr1oyWLVuya9cujX3ev6dLly5Nx44dcXV1pUCBAhrTms6ePZs+ffqwY8cO1q5dS2xsLLlz56Z27do8efKE8+fPf+/LFV+hXbt2Gp/d3NyoUaOGVv7du3c1PstzTYivp0j6sVKHDh0URVGUK1euKBEREYqDg4PG9mPHjik3b95Uf16wYIGiKIrSu3dvrWP16tVLURRFmTdvnka+u7u7cvPmTSU6Olr5559/NLZVrFhRURRFadq0qVadihcvnuzfj6Qvu58+/LebPn26oiiK0qpVqwT3e/z4sXLu3DmNvBo1aihxcXHK2rVrlf3792tsa9WqlaIoilK/fn2N/HHjxik+Pj5K48aNlbi4OMXFxUW9zdTUVAkODtY61ofHbNGihTpPURRlzpw5yf69Svp2KVu2bEpYWJhy584dxcbGRmNbhgwZlDt37ighISFKlixZFEj8np48ebKiKIrSvHlzdZ6tra0SFxenLFy4MMFzZ8yYMdmvX9LXpTlz5iiKonxWWXmuSZL05Um6V/3A/vzzTwwMDBg+fHiiZZycnOjSpQtHjhxh7ty5WtvnzZvH0aNH+fXXX3FyctLY5uHhwapVq+jatSsODg7fvP4iZTt69CgAWbNmTXD7qVOnKFq0KKampuq8cuXKcfv2bfbv30/p0qXR09PT2BYfH6/VDaFNmzZs2bKFPXv2EBwcTJs2bdTbIiMj2bZtG1WrViVjxoxadWjTpg0hISFab7jFj2XIkCGkSZOGbt26ERAQoLEtMDCQ7t27Y2lpyZAhQz56nJMnTwKQPXt2dV7WrFnR19dPsHsMgL+//1fWXqQm8lwT4stJ0PEDc3d3/2RQULt2bQwNDRNskn1r1apVGBkZUatWLa1tkyZNwtDQ8KOBzfvSpk1LhgwZNNLbPqkidXn7wywwMDDB7adOncLY2Fijz3u5cuU4c+YMZ86cIV26dBQoUEBj27179zS6C5QsWZKcOXOyfv16YmJi2LZtG23bttU4z9q1azEyMqJFixYa+dbW1tSsWZPt27cTGRmpsc3U1FTrPsyQIQNGRkZf9mWIZFW/fn3c3d3V3V8+dPLkSdzd3alfv/5Hj5MlSxYAXr16pc57+vQpoOrPb2Zm9m0qLFItea4J8XWSvblF0rdN73cdyJo1qxIdHa3MmjVLvf397lUzZ85UFEVRChcunOjxihQpoiiKokyfPl2d5+7uruzevVsBlKVLlyrh4eGKvb29Ah/vXpWQiIiIZP/OJH36fqpSpYqSIUMGxcnJSWnRooXi7++vvH79WnF0dExwv7x58yqKoigjR45UAMXAwEAJDQ1V2rdvrwCKt7e30rNnTwVQLCwslJiYGK0uLLNnz1aePn2q/lytWjWt+1VfX1/x9PRUTp8+rbFvt27dFEVRlOrVq2vkf0zLli2T/fuWpFuysrJSFEVRtm/f/tFyO3bsUBRFUSwsLBK8p5s0aaL4+voqERERipOTk8a+K1asUBRFUQIDA5WtW7cqAwcOVHLnzp3s1y7p2yRdulfJc02SpC9PMpD8B+fu7s7q1avp1q0bU6ZMwcfHR2O7paUlAKGhoYke4+02KyurBLdPnDiR9u3bM3z4cPr37//R+vTq1UtrUFtcXNynLkOkAEeOHNH47OHhQdu2bfHy8kqw/N27dwkICMDV1RWAwoULY2FhwZkzZwA4c+YM5cqVY/78+ZQpUwZDQ0ONN9UGBga0bNmSlStXqvOOHj2Kr68vbdu25fr16wDEx8ezYcMGBg4ciIuLi/rNdJs2bfDx8dGqN8COHTv43//+p5X/4axuIuX7nGfY+9vflgfte9rd3Z127drh6empkd+pUycuXLhA586dadKkCU2aNGHGjBkcOXIENze3RP8bED8eea4J8eUk6PgJfCwoSOgP8Yc+9Uf9w8DmYy5cuMDly5d1qL1IKd4GjLGxsfj6+nL//n0URSFNmjQaUzvGxcWp+9WfOXOGChUqoKenR7ly5fD19eXx48fqbX369AFUXRAAjT/ONWrUwNbWlgsXLmj0sT927BitW7dm2LBhqF5OqroiDBw4kDZt2jB58mScnJwoX748s2fPJj4+XutaXrx4keAfbZH6fM4z7O32+Ph4jTEfb+/ptGnT0rlzZypUqKAxa9VbiqIwb9485s2bR/r06SlXrhw9evSgTp06bNiwgQoVKnzbixLJTp5rQiSNZG9ukfRtU0IzsyxbtkzdBer97lVdu3ZVFEVRNw0nlNzc3BRFUZQuXbqo897vXgWq2WOio6OVv//+W2av+sHSp/7txo4dq9GU7+7urt42ZMgQRVEUpVChQsqGDRuUbdu2qbeVKVNGURRFcXR0VA4fPqy8ePFC47hr1qz5aJeBSpUqaZS/c+eOcuPGDQVQBg8erCiKovzyyy9a9VUUmeXlR0svXrxQHj9+/NEyT548UXdpSeie1tfXV86cOaO8ePFCSZMmzWed99ixY4qiKErmzJmT/TuQ9OUpoe5V8lyTJOnbJxlI/pOYOHEihoaGDBs2TCN///79xMbG0r59+0T3dXNzIyYmhgMHDiRa5smTJ6xZs4bu3bvLTFY/mVWrVlGtWjV1en9A5Pvz2pcrV05jBpfLly8TGRlJpUqVKFWqlMY2c3NzGjZsyIYNG2jWrJlW8vLySnDgZcGCBSlYsCBt2rThwYMHXLp0KYmvXqQEu3fvJlu2bOo3yx9ydXUla9asbN68OdFjxMfHM2LECJycnNRvqj/l7f0lz7wfjzzXhEgayR75SPq2KbE3029bO+7evauxTseiRYsURVGUHj16aB2re/fuiqIoyvz58zXyP2zpAFVrR0xMjHLlyhVFUaSl40dJX/NvZ2RkpISHhyunT59WFEVRypQpo7H99OnT6m19+/ZV57dt21ZRFEVxdXVN8LgLFy5UXr58qRgbG6vzsmTJoiiKakCxoijKmDFjEtxX3gj+eCl79uxKWFiYcuvWLSV9+vQa26ytrZVbt24pQUFBn1ynA1DOnTuneHt7KyYmJgqg2NnZKXnz5tUqZ2RkpFy5ckWJjY1VMmTIkOzfgaQvT7oMJH/7by/PNUmSdE8ypuMnMmnSJNq3b0+ePHm4deuWOn/AgAHkyZOH+fPnU6tWLXWLRs2aNWnUqBHHjx9n0KBBnzz+29aOjh07Jlqmdu3a5MmTRyv/zJkzCa5ULlK3mJgYLl68SIUKFYiMjNQaz3PmzBkGDx4MaPZ7btu2LQEBAerBmR/atWsX3bp1o27dumzfvh1QDWw/ffo0jRo1AlRvCBOTK1curTeKAL6+vhw+fFinaxTJ7/Hjx7i5ubF+/Xpu3ryptSK5tbU1rVq1wsPD45PHmjZtGlu2bKFjx44sXLgQZ2dnLly4wNGjRzly5Ag+Pj7Y2trSunVrihQpwt9//53otNHixyTPNSG+XLJHPpK+bfrYW7zly5criqJotHSA6s3Nb7/9ply8eFEJDQ1VwsLClEuXLin9+vVTDA0NtY6TUEsHqN44xsTEJNrSkZgOHTok+/cmSff76XPSpEmTFEVRlFOnTmlta9SokaIoihIcHKzo6+sroFrhOTo6Wlm5cmWixzQ1NVXCwsKUrVu3auT37NlTURRFzW3fWwAACMtJREFUa8Xg99PHHDt2LNm/b0lfnvLnz6+sWbNG8fLyUmJjYxVFUZTw8HCtloqP3dN6enrKw4cPlYcPHyr6+vqKhYWF0rdvX2X//v3Ks2fPlKioKCU4OFg5ffq0xjg3Sak36drSAfJckyTpS5Lem/8jhBBC/FDat2/PihUrWLNmDR06dEju6gghxE9NulcJIYT4Ia1evRoHBwf++usvXrx4wciRI5O7SkII8dOSlg4hhBBCCCFEkpIpc4UQQgghhBBJSoIOIYQQQgghRJKSoEMIIYQQQgiRpCToEEIIIYQQQiQpCTqEEEIIIYQQSUqCDiGEEEIIIUSSkqBDCCFSARcXFxRFYfny5Rr5x44dQ7WYctJwd3fH3d09yY4vhBDi5yBBhxBCfODtD/z3U1RUFM+ePWPt2rUULFgwuav4zSxfvhxFUXBxcUnuqgghhPiByYrkQgiRiEePHrFmzRoALCwsKF26NG3atKFJkyZUrVqVM2fOJHMNwc3NDXNz8yQ7ftWqVZPs2EIIIX4eEnQIIUQiHj16xB9//KGRN2HCBEaNGsWkSZOoXLlyMtXsnefPnyfp8Z88eZKkxxdCCPFzkO5VQgihgzlz5gBQokQJABRF4dixYzg6OrJy5Uq8vb2Ji4ujYsWK6n3Kly/Prl278Pf3JzIykgcPHjBhwgTMzMy0jq+vr8/QoUN5+PAhERERPHz4kOHDh6Ovn/Dj+mNjOho0aMDBgwcJCAggIiICd3d3Vq1aRf78+QHVeI2OHTsC4OHhoe5KduzYMfUxEhvTYW5uzrhx47h79y4REREEBgayZ88eypYtq1V27NixKIpCxYoVad26NVevXiU8PBwvLy9mzZqFqalpgvUXQgjx45CWDiGE+ALv/9DPkCEDZ8+e5eXLl2zYsAFTU1NCQkIA6NGjB3PnziUoKIjdu3fj5+fHL7/8wqhRo6hcuTKVK1cmJiZGfaxFixbRpUsXnjx5wty5czE1NWXgwIEJ/pj/mOnTpzNo0CACAwPZsWMHfn5+ZMqUiWrVqnH58mVu377NrFmz6NixI0WKFGHWrFkEBQUBqgDkY0xMTDh69CilSpXi8uXLzJo1Czs7O1q2bEnNmjVp3bo1W7Zs0dqvT58+1KpVi507d3L06FFq1arFb7/9ho2NDe3atdPp+oQQQqQ+iiRJkiRJepdcXFwURVGU/fv3a20bN26coiiKcuTIEQVQ3lq6dKmir6+vUTZv3rxKdHS0cvXqVSV9+vQa24YNG6YoiqIMHDhQnVexYkVFURTl6tWrirm5uTrf0dFR8fPzUxRFUZYvX65xnGPHjimKKgJSp7p16yqKoijXr1/XOq+BgYFia2ur/rx8+XJFURTFxcUlwe/C3d1dcXd318gbPXq0oiiKsnr1ao38IkWKKJGRkcrLly8VCwsLdf7YsWMVRVGUV69eKbly5VLnm5qaKvfu3VNiY2MVBweHZP93lyRJkiRJSZqSvQKSJEmSlKLS26Dj4cOHytixY5WxY8cqU6dOVU6cOKEoiqKEh4crpUuXVkAVdERGRioZMmTQOs6sWbMURVEUV1dXrW16enqKr6+vcvHiRXXe0qVLFUVRlMaNG2uVHzly5GcHHXv37lUURVEqVar0yWv9kqDj0aNHSlRUlOLk5KRVfuHChYqiKEq7du3UeW+DjnHjxmmVf7utXr16yf7vLkmSJEmSki5J9yohhEhEjhw5GDduHADR0dH4+vqydu1apkyZwq1bt9Tl3N3dCQwM1Nq/dOnSANSsWTPBWaBiYmLIkyeP+nPhwoUBOHnypFbZhPISU7JkSSIjIzlx4sRn7/O5LC0tyZ49O3fu3MHT01Nr+7Fjx+jWrRtFihRRz/z11uXLl7XKv3jxAoB06dJ987oKIYRIOSToEEKIRBw4cIDatWt/spyvr2+C+enTpwdg1KhRn3W+tGnTEhcXR0BAwGefI7HjeHp6JsmigVZWVh+tj7e3t0a5970d5/K+2NhYAAwMDL5VFYUQQqRAMnuVEEJ8pcR+3L/9kW1paYmenl6i6a3g4GAMDAywsbHROpadnd1n1ycoKAh7e3uNY38rb68psfrY29trlBNCCCFAgg4hhEgy58+fB951s/qU69evA6opdj+UUF5iLly4gKmpqca0vYmJi4sDPr+lITQ0lMePH5MjRw4cHR21tleqVAmAa9eufXZ9hRBC/Pgk6BBCiCQyb948YmJimDNnDpkyZdLanjZtWooUKaL+vHr1agDGjBmjscq4o6Mjv/3222efd+7cuQD8888/WFtba2wzMDDA1tZW/fnly5cACdYvMStXrsTY2JjJkydr5BcsWJCOHTsSFBTEjh07Pvt4QgghfnwypkMIIZLI7du36dWrF/Pnz+f+/fvs27ePx48fY2lpSbZs2ahYsSIrVqygZ8+eABw/fpxly5bRuXNnbt68yfbt2zExMaFly5acO3eO+vXrf9Z59+/fz7Rp0xgyZAgPHz5k+/bt+Pn54eTkRNWqVZk+fTr//PMPAEePHmXIkCEsWrSIrVu38vr1a54+fao1CPx9U6dOpW7duri5uZE3b16OHDmCra0tLVu2xNDQkK5duxIWFvb1X6AQQogfSrJPoSVJkiRJKSl9bJ2OD5OiKMqxY8f+394d4iYQRVEApUiWgSFB4xCsgRXMJkjAYtgHhh0g2QELgGSCQmGQMOrWtE0amqbmpeac5CaTzDM/o66Y93+dmUwm2e12uV6v6bout9stx+Mxm80mo9Ho22y/389yuUzbtnk+n2nbNqvVKsPh8M8rcz8zn89zOBxyv9/zeDxyuVyy3W4zHo+/zS0Wi5zP53Rd93Ken1bm9nq9DAaDrNfrnE6nr7s59vt9ptPpy+znWtzZbPbyrmmaJEnTNP/+3UVEpC5vHw8AAAAl/NMBAACUUjoAAIBSSgcAAFBK6QAAAEopHQAAQCmlAwAAKKV0AAAApZQOAACglNIBAACUUjoAAIBSSgcAAFBK6QAAAEopHQAAQKl3IqNKL7vzkO4AAAAASUVORK5CYII=",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.Image(filename=params.job_dir / \"confusion_matrix_test.png\", width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export model to TF Lite / TFLM\n",
    "\n",
    "\n",
    "Once we have trained and evaluated the model, we need to export the model into a format that can be used for inference on the edge. Currently, we export the model to TensorFlow Lite flatbuffer format. This will also generate a C header file that can be used with TensorFlow Lite for Microcontrollers (TFLM).\n",
    "\n",
    "\n",
    "### Post-Training Quantization (PTQ)\n",
    "\n",
    "For running on bare metal, we will perform post-training quantization to convert the model to an 8-bit integer model. The weights and activations will be quantized to 8-bits and biases will be quantized to 32-bits. This will reduce the model size and improve the inference speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantization = hk.QuantizationParams(\n",
    "    enabled=True,\n",
    "    format=\"INT8\",\n",
    "    io_type=\"int8\",\n",
    "    conversion=\"CONCRETE\",\n",
    ")\n",
    "params.quantization = quantization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Creating synthetic dataset cache with <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5000</span> patients                                   <a href=\"file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">ecg_synthetic.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">159</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Creating synthetic dataset cache with \u001b[1;36m5000\u001b[0m patients                                   \u001b]8;id=878050;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\u001b\\\u001b[2mecg_synthetic.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=80552;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\u001b\\\u001b[2m159\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[08/16/24 20:11:32] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> WARNING:absl:Please consider providing the trackable_obj argument in the  <a href=\"file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/tensorflow/lite/python/lite.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">lite.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/tensorflow/lite/python/lite.py#2166\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2166</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         from_concrete_functions. Providing without the trackable_obj argument is  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         deprecated and it will use the deprecated conversion path.                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[08/16/24 20:11:32]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m WARNING:absl:Please consider providing the trackable_obj argument in the  \u001b]8;id=858712;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/tensorflow/lite/python/lite.py\u001b\\\u001b[2mlite.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=480914;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/tensorflow/lite/python/lite.py#2166\u001b\\\u001b[2m2166\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m                    \u001b[0m         from_concrete_functions. Providing without the trackable_obj argument is  \u001b[2m            \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         deprecated and it will use the deprecated conversion path.                \u001b[2m            \u001b[0m\n"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1723839092.566023  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723839092.566111  758191 devices.cc:67] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n",
      "I0000 00:00:1723839092.566407  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723839092.566464  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723839092.566510  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723839092.566580  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723839092.566627  758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "W0000 00:00:1723839092.671832  758191 tf_tfl_flatbuffer_helpers.cc:392] Ignored output_format.\n",
      "W0000 00:00:1723839092.671846  758191 tf_tfl_flatbuffer_helpers.cc:395] Ignored drop_control_dependency.\n",
      "fully_quantize: 0, inference_type: 6, input_inference_type: INT8, output_inference_type: INT8\n",
      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>TF METRICS<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">LOSS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4022</span> <span style=\"color: #808000; text-decoration-color: #808000\">ACC</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8683</span> <span style=\"color: #808000; text-decoration-color: #808000\">F1</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8694</span> <span style=\"color: #808000; text-decoration-color: #808000\">IOU</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7551</span>                                     <a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">export.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py#105\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">105</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mTF METRICS\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4022\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8683\u001b[0m \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8694\u001b[0m \u001b[33mIOU\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.7551\u001b[0m                                     \u001b]8;id=339944;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=960343;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py#105\u001b\\\u001b[2m105\u001b[0m\u001b]8;;\u001b\\\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>TFL METRICS<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">LOSS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4077</span> <span style=\"color: #808000; text-decoration-color: #808000\">ACC</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8686</span> <span style=\"color: #808000; text-decoration-color: #808000\">F1</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8676</span> <span style=\"color: #808000; text-decoration-color: #808000\">IOU</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7529</span>                                    <a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">export.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py#106\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">106</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mTFL METRICS\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4077\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8686\u001b[0m \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8676\u001b[0m \u001b[33mIOU\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.7529\u001b[0m                                    \u001b]8;id=861404;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=138484;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py#106\u001b\\\u001b[2m106\u001b[0m\u001b]8;;\u001b\\\n"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Validation passed <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0055</span><span style=\"font-weight: bold\">)</span>                                                                   <a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">export.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py#114\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">114</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Validation passed \u001b[1m(\u001b[0m\u001b[1;36m0.0055\u001b[0m\u001b[1m)\u001b[0m                                                                   \u001b]8;id=743127;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=94135;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py#114\u001b\\\u001b[2m114\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TF dumps a lot of info to stdout, so we redirect it to /dev/null\n",
    "with open(os.devnull, 'w') as devnull:\n",
    "    with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n",
    "        task.export(params)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run inference demo\n",
    "\n",
    "We will run a demo on the PC to verify that the model is working as expected. The demo will load the model and run inferences across a randomly selected ECG signal. The demo will also provide the model's prediction and the corresponding class name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Inference: 100%|██████████| 4/4 [00:00<00:00,  5.55it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task.demo(params=params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
